Publications Implemented or Referenced by ELKI

The following publications are cited by classes in ELKI (as of ELKI 0.7.5):

de.lmu.ifi.dbs.elki.algorithm.DependencyDerivator
Elke Achtert, Christian Böhm, Hans-Peter Kriegel, Peer Kröger, Arthur Zimek
Deriving Quantitative Dependencies for Correlation Clusters
In: Proc. 12th Int. Conf. on Knowledge Discovery and Data Mining (KDD ‘06)
DOI:10.1145/1150402.1150408
DBLP:conf/kdd/AchtertBKKZ06

de.lmu.ifi.dbs.elki.algorithm.KNNDistancesSampler,
de.lmu.ifi.dbs.elki.algorithm.clustering.DBSCAN,
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.EpsilonNeighborPredicate,
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.MinPtsCorePredicate,
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.SimilarityNeighborPredicate
Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu
A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise
In: Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining (KDD ‘96)
Online: http://www.aaai.org/Library/KDD/1996/kdd96-037.php
DBLP:conf/kdd/EsterKSX96

de.lmu.ifi.dbs.elki.algorithm.KNNDistancesSampler,
de.lmu.ifi.dbs.elki.algorithm.clustering.DBSCAN
Erich Schubert, Jörg Sander, Martin Ester, Hans-Peter Kriegel, Xiaowei Xu
DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN
In: ACM Trans. Database Systems (TODS)
DOI:10.1145/3068335
DBLP:journals/tods/SchubertSEKX17

de.lmu.ifi.dbs.elki.algorithm.clustering.CanopyPreClustering
A. McCallum, K. Nigam, L. H. Ungar
Efficient Clustering of High Dimensional Data Sets with Application to Reference Matching
In: Proc. 6th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining
DOI:10.1145/347090.347123
DBLP:conf/kdd/McCallumNU00

de.lmu.ifi.dbs.elki.algorithm.clustering.GriDBSCAN
S. Mahran, K. Mahar
Using grid for accelerating density-based clustering
In: 8th IEEE Int. Conf. on Computer and Information Technology
DOI:10.1109/CIT.2008.4594646
DBLP:conf/IEEEcit/MahranM08

de.lmu.ifi.dbs.elki.algorithm.clustering.Leader
J. A. Hartigan
Chapter 3: Quick Partition Algorithms, 3.2 Leader Algorithm
In: Clustering algorithms
Online: http://dl.acm.org/citation.cfm?id=540298

de.lmu.ifi.dbs.elki.algorithm.clustering.NaiveMeanShiftClustering
Y. Cheng
Mean shift, mode seeking, and clustering
In: IEEE Transactions on Pattern Analysis and Machine Intelligence 17-8
DOI:10.1109/34.400568
DBLP:journals/pami/Cheng95

de.lmu.ifi.dbs.elki.algorithm.clustering.SNNClustering
L. Ertöz, M. Steinbach, V. Kumar
Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data
In: Proc. of SIAM Data Mining (SDM’03)
DOI:10.1137/1.9781611972733.5
DBLP:conf/sdm/ErtozSK03

de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation.AffinityPropagationClusteringAlgorithm
B. J. Frey, D. Dueck
Clustering by Passing Messages Between Data Points
In: Science Vol 315
DOI:10.1126/science.1136800

de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering.ChengAndChurch
Y. Cheng, G. M. Church
Biclustering of expression data
In: Proc. 8th Int. Conf. on Intelligent Systems for Molecular Biology (ISMB)
Online: http://www.aaai.org/Library/ISMB/2000/ismb00-010.php
DBLP:conf/ismb/ChengC00

de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.CASH
Elke Achtert, Christian Böhm, Jörn David, Peer Kröger, Arthur Zimek
Robust clustering in arbitraily oriented subspaces
In: Proc. 8th SIAM Int. Conf. on Data Mining (SDM’08)
DOI:10.1137/1.9781611972788.69
DBLP:conf/sdm/AchtertBDKZ08

de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.COPAC,
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.COPACNeighborPredicate
Elke Achtert, Christian Böhm, Hans-Peter Kriegel, Peer Kröger, Arthur Zimek
Robust, Complete, and Efficient Correlation Clustering
In: Proc. 7th SIAM Int. Conf. on Data Mining (SDM’07)
DOI:10.1137/1.9781611972771.37
DBLP:conf/sdm/AchtertBKKZ07

de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.ERiC,
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.ERiCNeighborPredicate
Elke Achtert, Christian Böhm, Hans-Peter Kriegel, Peer Kröger, Arthur Zimek
On Exploring Complex Relationships of Correlation Clusters
In: Proc. 19th Int. Conf. Scientific and Statistical Database Management (SSDBM 2007)
DOI:10.1109/SSDBM.2007.21
DBLP:conf/ssdbm/AchtertBKKZ07

de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.FourC,
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.FourCCorePredicate,
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.FourCNeighborPredicate
Christian Böhm, Karin Kailing, Peer Kröger, Arthur Zimek
Computing Clusters of Correlation Connected Objects
In: Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD 2004)
DOI:10.1145/1007568.1007620
DBLP:conf/sigmod/BohmKKZ04

de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.HiCO
Elke Achtert, Christian Böhm, Peer Kröger, Arthur Zimek
Mining Hierarchies of Correlation Clusters
In: Proc. Int. Conf. on Scientific and Statistical Database Management (SSDBM’06)
DOI:10.1109/SSDBM.2006.35
DBLP:conf/ssdbm/AchtertBKZ06

de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.LMCLUS
R. Haralick, R. Harpaz
Linear manifold clustering in high dimensional spaces by stochastic search
In: Pattern Recognition volume 40, Issue 10
DOI:10.1016/j.patcog.2007.01.020
DBLP:journals/pr/HaralickH07

de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.ORCLUS
C. C. Aggarwal, P. S. Yu
Finding Generalized Projected Clusters in High Dimensional Spaces
In: Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD ‘00)
DOI:10.1145/342009.335383
DBLP:conf/sigmod/AggarwalY00

de.lmu.ifi.dbs.elki.algorithm.clustering.em.EM
C. Fraley, A. E. Raftery
Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering
In: J. Classification 24(2)
DOI:10.1007/s00357-007-0004-5
DBLP:journals/classification/FraleyR07

de.lmu.ifi.dbs.elki.algorithm.clustering.em.EM
A. P. Dempster, N. M. Laird, D. B. Rubin
Maximum Likelihood from Incomplete Data via the EM algorithm
In: Journal of the Royal Statistical Society, Series B, 39(1)
Online: http://www.jstor.org/stable/2984875

de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.GeneralizedDBSCAN
Jörg Sander, Martin Ester, Hans-Peter Kriegel, Xiaowei Xu
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
In: Data Mining and Knowledge Discovery
DOI:10.1023/A:1009745219419
DBLP:journals/datamine/SanderEKX98

de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.LSDBC
E. Biçici, D. Yuret
Locally Scaled Density Based Clustering
In: Adaptive and Natural Computing Algorithms
DOI:10.1007/978-3-540-71618-1_82
DBLP:conf/icannga/BiciciY07

de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.PreDeConCorePredicate,
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.PreDeConNeighborPredicate,
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.PreDeCon
Christian Böhm, Karin Kailing, Hans-Peter Kriegel, Peer Kröger
Density Connected Clustering with Local Subspace Preferences
In: Proc. 4th IEEE Int. Conf. on Data Mining (ICDM’04)
DOI:10.1109/ICDM.2004.10087
DBLP:conf/icdm/BohmKKK04

de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.parallel.ParallelGeneralizedDBSCAN
closely related
M. Patwary, D. Palsetia, A. Agrawal, W. K. Liao, F. Manne, A. Choudhary
A new scalable parallel DBSCAN algorithm using the disjoint-set data structure
In: IEEE Int. Conf. for High Performance Computing, Networking, Storage and Analysis (SC)
DOI:10.1109/SC.2012.9
DBLP:conf/sc/PatwaryPALMC12

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.AGNES,
tutorial.clustering.NaiveAgglomerativeHierarchicalClustering3,
tutorial.clustering.NaiveAgglomerativeHierarchicalClustering4
R. M. Cormack
A Review of Classification
In: Journal of the Royal Statistical Society. Series A, Vol. 134, No. 3
DOI:10.2307/2344237

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.AGNES
L. Kaufman, P. J. Rousseeuw
Agglomerative Nesting (Program AGNES)
In: Finding Groups in Data: An Introduction to Cluster Analysis
DOI:10.1002/9780470316801.ch5

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.AGNES
P. H. Sneath
The application of computers to taxonomy
In: Journal of general microbiology, 17(1)
DOI:10.1099/00221287-17-1-201

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.AbstractHDBSCAN,
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.HDBSCANLinearMemory,
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.SLINKHDBSCANLinearMemory,
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction,
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction.SimplifiedHierarchyExtraction
R. J. G. B. Campello, D. Moulavi, J. Sander
Density-Based Clustering Based on Hierarchical Density Estimates
In: Pacific-Asia Conf. Advances in Knowledge Discovery and Data Mining (PAKDD)
DOI:10.1007/978-3-642-37456-2_14
DBLP:conf/pakdd/CampelloMS13

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.AnderbergHierarchicalClustering,
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.MiniMaxAnderberg
M. R. Anderberg
Hierarchical Clustering Methods
In: Cluster Analysis for Applications

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.CLINK
D. Defays
An Efficient Algorithm for the Complete Link Cluster Method
In: The Computer Journal 20.4
DOI:10.1093/comjnl/20.4.364
DBLP:journals/cj/Defays77

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.MiniMax
S. I. Ao, K. Yip, M. Ng, D. Cheung, P.-Y. Fong, I. Melhado, P. C. Sham
CLUSTAG: hierarchical clustering and graph methods for selecting tag SNPs
In: Bioinformatics, 21 (8)
DOI:10.1093/bioinformatics/bti201
DBLP:journals/bioinformatics/AoYNCFMS05

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.MiniMax
J. Bien, R. Tibshirani
Hierarchical Clustering with Prototypes via Minimax Linkage
In: Journal of the American Statistical Association 106(495)
DOI:10.1198/jasa.2011.tm10183

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.MiniMaxNNChain,
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.NNChain
F. Murtagh
A survey of recent advances in hierarchical clustering algorithms
In: The Computer Journal 26(4)
DOI:10.1093/comjnl/26.4.354
DBLP:journals/cj/Murtagh83

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.MiniMaxNNChain,
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.NNChain
D. Müllner
Modern hierarchical, agglomerative clustering algorithms
In: arXiv preprint arXiv:1109.2378
Online: https://arxiv.org/abs/1109.2378
DBLP:journals/corr/abs-1109-2378

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.SLINK
R. Sibson
SLINK: An optimally efficient algorithm for the single-link cluster method
In: The Computer Journal 16 (1973), No. 1, p. 30-34.
DOI:10.1093/comjnl/16.1.30
DBLP:journals/cj/Sibson73

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch.AverageInterclusterDistance,
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch.AverageIntraclusterDistance,
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch.CentroidEuclideanDistance,
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch.CentroidManhattanDistance,
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch.VarianceIncreaseDistance
T. Zhang
Data Clustering for Very Large Datasets Plus Applications
In: University of Wisconsin Madison, Technical Report #1355
Online: ftp://ftp.cs.wisc.edu/pub/techreports/1997/TR1355.pdf

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch.BIRCHLeafClustering,
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch.CFTree
T. Zhang, R. Ramakrishnan, M. Livny
BIRCH: A New Data Clustering Algorithm and Its Applications
In: Data Min. Knowl. Discovery
DOI:10.1023/A:1009783824328
DBLP:journals/datamine/ZhangRL97

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch.BIRCHLeafClustering,
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch.CFTree,
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch.DiameterCriterion,
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.birch.RadiusCriterion
T. Zhang, R. Ramakrishnan, M. Livny
BIRCH: An Efficient Data Clustering Method for Very Large Databases
In: Proc. 1996 ACM SIGMOD International Conference on Management of Data
DOI:10.1145/233269.233324
DBLP:conf/sigmod/ZhangRL96

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.extraction.ClustersWithNoiseExtraction
Erich Schubert, Michael Gertz
Semantic Word Clouds with Background Corpus Normalization and t-distributed Stochastic Neighbor Embedding
In: ArXiV preprint, 1708.03569
Online: http://arxiv.org/abs/1708.03569
DBLP:journals/corr/abs-1708-03569

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.linkage.CentroidLinkage,
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.linkage.MedianLinkage
J. C. Gower
A comparison of some methods of cluster analysis
In: Biometrics (1967)
DOI:10.2307/2528417

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.linkage.CompleteLinkage,
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.linkage.FlexibleBetaLinkage,
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.linkage.Linkage
G. N. Lance, W. T. Williams
A general theory of classificatory sorting strategies 1. Hierarchical systems
In: The Computer Journal 9.4
DOI:10.1093/comjnl/9.4.373

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.linkage.CompleteLinkage
T. Sørensen
A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons
In: Biologiske Skrifter 5 (4)

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.linkage.CompleteLinkage
S. C. Johnson
Hierarchical clustering schemes
In: Psychometrika 32
DOI:10.1007/BF02289588

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.linkage.CompleteLinkage
P. Macnaughton-Smith
Some statistical and other numerical techniques for classifying individuals
In: Home Office Res. Rpt. No. 6, H.M.S.O., London

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.linkage.GroupAverageLinkage,
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.linkage.WeightedAverageLinkage
R. R. Sokal, C. D. Michener
A statistical method for evaluating systematic relationship
In: University of Kansas science bulletin 28
Online: https://archive.org/details/cbarchive_33927_astatisticalmethodforevaluatin1902

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.linkage.MinimumVarianceLinkage
E. Diday, J. Lemaire, J. Pouget, F. Testu
Elements d’analyse de donnees

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.linkage.MinimumVarianceLinkage
J. Podani
New Combinatorial Clustering Methods
In: Vegetatio 81(1/2)
DOI:10.1007/978-94-009-2432-1_5

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.linkage.SingleLinkage
K. Florek, J. Łukaszewicz, J. Perkal, H. Steinhaus, S. Zubrzycki
Sur la liaison et la division des points d’un ensemble fini
In: Colloquium Mathematicae 2(3-4)

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.linkage.WardLinkage
D. Wishart
256. Note: An Algorithm for Hierarchical Classifications
In: BBiometrics 25(1)
DOI:10.2307/2528688

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.linkage.WardLinkage
J. H. Ward Jr.
Hierarchical grouping to optimize an objective function
In: Journal of the American statistical association 58.301
DOI:10.1080/01621459.1963.10500845

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.CLARA
L. Kaufman, P. J. Rousseeuw
Clustering Large Applications (Program CLARA)
In: Finding Groups in Data: An Introduction to Cluster Analysis
DOI:10.1002/9780470316801.ch3

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.CLARA
L. Kaufman, P. J. Rousseeuw
Clustering Large Data Sets
In: Pattern Recognition in Practice
DOI:10.1016/B978-0-444-87877-9.50039-X

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.CLARANS
R. T. Ng, J. Han
CLARANS: a method for clustering objects for spatial data mining
In: IEEE Transactions on Knowledge and Data Engineering 14(5)
DOI:10.1109/TKDE.2002.1033770
DBLP:journals/tkde/NgH02

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.FastCLARA,
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.FastCLARANS,
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsFastPAM,
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsFastPAM1,
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.LABInitialMeans
Erich Schubert, Peter J. Rousseeuw
Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithms
In: preprint, to appear
Online: https://arxiv.org/abs/1810.05691
DBLP:journals/corr/abs-1810-05691

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansAnnulus
G. Hamerly and J. Drake
Accelerating Lloyd’s Algorithm for k-Means Clustering
In: Partitional Clustering Algorithms
DOI:10.1007/978-3-319-09259-1_2

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansAnnulus
J. Drake
Faster k-means clustering
In: Faster k-means clustering
Online: http://hdl.handle.net/2104/8826

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansBisecting,
de.lmu.ifi.dbs.elki.evaluation.clustering.SetMatchingPurity
M. Steinbach, G. Karypis, V. Kumar
A Comparison of Document Clustering Techniques
In: KDD workshop on text mining. Vol. 400. No. 1
Online: http://glaros.dtc.umn.edu/gkhome/fetch/papers/docclusterKDDTMW00.pdf

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansCompare,
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansSort
S. J. Phillips
Acceleration of k-means and related clustering algorithms
In: Proc. 4th Int. Workshop on Algorithm Engineering and Experiments (ALENEX 2002)
DOI:10.1007/3-540-45643-0_13
DBLP:conf/alenex/Phillips02

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansElkan
C. Elkan
Using the triangle inequality to accelerate k-means
In: Proc. 20th International Conference on Machine Learning, ICML 2003
Online: http://www.aaai.org/Library/ICML/2003/icml03-022.php
DBLP:conf/icml/Elkan03

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansExponion,
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansSimplifiedElkan
J. Newling
Fast k-means with accurate bounds
In: Proc. 33nd Int. Conf. on Machine Learning, ICML 2016
Online: http://jmlr.org/proceedings/papers/v48/newling16.html
DBLP:conf/icml/NewlingF16

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansHamerly
G. Hamerly
Making k-means even faster
In: Proc. 2010 SIAM International Conference on Data Mining
DOI:10.1137/1.9781611972801.12
DBLP:conf/sdm/Hamerly10

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansLloyd,
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.RandomlyChosenInitialMeans
E. W. Forgy
Cluster analysis of multivariate data: efficiency versus interpretability of classifications
In: Biometrics 21(3)

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansLloyd
S. Lloyd
Least squares quantization in PCM
In: IEEE Transactions on Information Theory 28 (2): 129–137.
DOI:10.1109/TIT.1982.1056489
DBLP:journals/tit/Lloyd82

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansMacQueen,
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.FirstKInitialMeans
J. MacQueen
Some Methods for Classification and Analysis of Multivariate Observations
In: 5th Berkeley Symp. Math. Statist. Prob.
Online: http://projecteuclid.org/euclid.bsmsp/1200512992

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansMinusMinus
S. Chawla, A. Gionis
k-means–: A Unified Approach to Clustering and Outlier Detection
In: Proc. 13th SIAM Int. Conf. on Data Mining (SDM 2013)
DOI:10.1137/1.9781611972832.21
DBLP:conf/sdm/ChawlaG13

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMediansLloyd
P. S. Bradley, O. L. Mangasarian, W. N. Street
Clustering via Concave Minimization
In: Advances in Neural Information Processing Systems
Online: https://papers.nips.cc/paper/1260-clustering-via-concave-minimization
DBLP:conf/nips/BradleyMS96

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPAM,
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.PAMInitialMeans
L. Kaufman, P. J. Rousseeuw
Clustering by means of Medoids
In: Statistical Data Analysis Based on the L1-Norm and Related Methods

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPAM,
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.PAMInitialMeans
L. Kaufman, P. J. Rousseeuw
Partitioning Around Medoids (Program PAM)
In: Finding Groups in Data: An Introduction to Cluster Analysis
DOI:10.1002/9780470316801.ch2

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPAMReynolds,
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPark
A. P. Reynolds, G. Richards, B. de la Iglesia, V. J. Rayward-Smith
Clustering Rules: A Comparison of Partitioning and Hierarchical Clustering Algorithms
In: J. Math. Model. Algorithms 5(4)
DOI:10.1007/s10852-005-9022-1
DBLP:journals/jmma/ReynoldsRIR06

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPark,
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.ParkInitialMeans
H.-S. Park, C.-H. Jun
A simple and fast algorithm for K-medoids clustering
In: Expert Systems with Applications 36(2)
DOI:10.1016/j.eswa.2008.01.039
DBLP:journals/eswa/ParkJ09

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.XMeans,
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality.AbstractKMeansQualityMeasure,
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality.AkaikeInformationCriterion
D. Pelleg, A. Moore
X-means: Extending K-means with Efficient Estimation on the Number of Clusters
In: Proc. 17th Int. Conf. on Machine Learning (ICML 2000)
Online: http://www.pelleg.org/shared/hp/download/xmeans.ps
DBLP:conf/icml/PellegM00

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.KMeansPlusPlusInitialMeans
D. Arthur, S. Vassilvitskii
k-means++: the advantages of careful seeding
In: Proc. 18th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2007)
Online: http://dl.acm.org/citation.cfm?id=1283383.1283494
DBLP:conf/soda/ArthurV07

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.OstrovskyInitialMeans
R. Ostrovsky, Y. Rabani, L. J. Schulman, C. Swamy
The effectiveness of Lloyd-type methods for the k-means problem
In: Symposium on Foundations of Computer Science (FOCS)
DOI:10.1109/FOCS.2006.75
DBLP:conf/focs/OstrovskyRSS062

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.OstrovskyInitialMeans
R. Ostrovsky, Y. Rabani, L. J. Schulman, C. Swamy
The effectiveness of lloyd-type methods for the k-means problem
In: Journal of the ACM 59(6)
DOI:10.1145/2395116.2395117
DBLP:journals/jacm/OstrovskyRSS12

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.RandomNormalGeneratedInitialMeans,
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.RandomUniformGeneratedInitialMeans
R. C. Jancey
Multidimensional group analysis
In: Australian Journal of Botany 14(1)
DOI:10.1071/BT9660127

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.RandomlyChosenInitialMeans
D. J. McRae
MIKCA: A FORTRAN IV Iterative K-Means Cluster Analysis Program
In: Behavioral Science 16(4)

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.RandomlyChosenInitialMeans
M. R. Anderberg
Nonhierarchical Clustering Methods
In: Cluster Analysis for Applications

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.SampleKMeansInitialization
P. S. Bradley, U. M. Fayyad
Refining Initial Points for K-Means Clustering
In: Proc. 15th Int. Conf. on Machine Learning (ICML 1998)
DBLP:conf/icml/BradleyF98

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality.AkaikeInformationCriterion
H. Akaike
Information Theory and an Extension of the Maximum Likelihood Principle
In: Second International Symposium on Information Theory

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality.BayesianInformationCriterion
G. Schwarz
Estimating the dimension of a model
In: The annals of statistics 6.2
DOI:10.1214/aos/1176344136

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality.BayesianInformationCriterionZhao
Q. Zhao, M. Xu, P. Fränti
Knee Point Detection on Bayesian Information Criterion
In: 20th IEEE International Conference on Tools with Artificial Intelligence
DOI:10.1109/ICTAI.2008.154
DBLP:conf/ictai/ZhaoXF08

de.lmu.ifi.dbs.elki.algorithm.clustering.optics.AbstractOPTICS,
de.lmu.ifi.dbs.elki.algorithm.clustering.optics.OPTICSHeap,
de.lmu.ifi.dbs.elki.algorithm.clustering.optics.OPTICSList,
de.lmu.ifi.dbs.elki.algorithm.clustering.optics.OPTICSXi
Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, Jörg Sander
OPTICS: Ordering Points to Identify the Clustering Structure
In: Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD ‘99)
DOI:10.1145/304181.304187
DBLP:conf/sigmod/AnkerstBKS99

de.lmu.ifi.dbs.elki.algorithm.clustering.optics.DeLiClu
Elke Achtert, Christian Böhm, Peer Kröger
DeLiClu: Boosting Robustness, Completeness, Usability, and Efficiency of Hierarchical Clustering by a Closest Pair Ranking
In: Proc. 10th Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD 2006)
DOI:10.1007/11731139_16
DBLP:conf/pakdd/AchtertBK06

de.lmu.ifi.dbs.elki.algorithm.clustering.optics.FastOPTICS,
de.lmu.ifi.dbs.elki.index.preprocessed.fastoptics.RandomProjectedNeighborsAndDensities
J. Schneider, M. Vlachos
Fast parameterless density-based clustering via random projections
In: Proc. 22nd ACM Int. Conf. on Information & Knowledge Management (CIKM 2013)
DOI:10.1145/2505515.2505590
DBLP:conf/cikm/SchneiderV13

de.lmu.ifi.dbs.elki.algorithm.clustering.optics.OPTICSXi
Erich Schubert, Michael Gertz
Improving the Cluster Structure Extracted from OPTICS Plots
In: Proc. Lernen, Wissen, Daten, Analysen (LWDA 2018)
Online: http://ceur-ws.org/Vol-2191/paper37.pdf
DBLP:conf/lwa/SchubertG18

de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.CLIQUE
R. Agrawal, J. Gehrke, D. Gunopulos, P. Raghavan
Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications
In: Proc. SIGMOD Conference, Seattle, WA, 1998
DOI:10.1145/276304.276314
DBLP:conf/sigmod/AgrawalGGR98

de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.DOC,
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.FastDOC
C. M. Procopiuc, M. Jones, P. K. Agarwal, T. M. Murali
A Monte Carlo algorithm for fast projective clustering
In: Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD ‘02)
DOI:10.1145/564691.564739
DBLP:conf/sigmod/ProcopiucJAM02

de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.DiSH
E. Achtert, C. Böhm, H.-P. Kriegel, P. Kröger, I. Müller-Gorman, A. Zimek
Detection and Visualization of Subspace Cluster Hierarchies
In: Proc. 12th Int. Conf. on Database Systems for Advanced Applications (DASFAA)
DOI:10.1007/978-3-540-71703-4_15
DBLP:conf/dasfaa/AchtertBKKMZ07

de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.HiSC,
de.lmu.ifi.dbs.elki.index.preprocessed.preference.HiSCPreferenceVectorIndex
Elke Achtert, Christian Böhm, Hans-Petre Kriegel, Peer Kröger, Ina Müller-Gorman, Arthur Zimek
Finding Hierarchies of Subspace Clusters
In: Proc. 10th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD’06)
DOI:10.1007/11871637_42
DBLP:conf/pkdd/AchtertBKKMZ06

de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.P3C
Gabriela Moise, Jörg Sander, Martin Ester
P3C: A Robust Projected Clustering Algorithm
In: Proc. Sixth International Conference on Data Mining (ICDM ‘06)
DOI:10.1109/ICDM.2006.123
DBLP:conf/icdm/MoiseSE06

de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.PROCLUS
C. C. Aggarwal, C. Procopiuc, J. L. Wolf, P. S. Yu, J. S. Park
Fast Algorithms for Projected Clustering
In: Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD ‘99)
DOI:10.1145/304181.304188

de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.SUBCLU
Karin Kailing, Hans-Peter Kriegel, Peer Kröger
Density Connected Subspace Clustering for High Dimensional Data
In: Proc. SIAM Int. Conf. on Data Mining (SDM’04)
DOI:10.1137/1.9781611972740.23
DBLP:conf/sdm/KroegerKK04

de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.CKMeans
S. D. Lee, B. Kao, R. Cheng
Reducing UK-means to K-means
In: ICDM Data Mining Workshops, 2007
DOI:10.1109/ICDMW.2007.40
DBLP:conf/icdm/LeeKC07

de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.CenterOfMassMetaClustering
Erich Schubert, Alexander Koos, Tobias Emrich, Andreas Züfle, Klaus Arthur Schmid, Arthur Zimek
A Framework for Clustering Uncertain Data
In: Proceedings of the VLDB Endowment, 8(12)
Online: http://www.vldb.org/pvldb/vol8/p1976-schubert.pdf
DBLP:journals/pvldb/SchubertKEZSZ15

de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.FDBSCAN,
de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.FDBSCANNeighborPredicate
Hans-Peter Kriegel, Martin Pfeifle
Density-based clustering of uncertain data
In: Proc. 11th ACM Int. Conf. on Knowledge Discovery and Data Mining (SIGKDD)
DOI:10.1145/1081870.1081955
DBLP:conf/kdd/KriegelP05

de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.RepresentativeUncertainClustering
Andreas Züfle, Tobias Emrich, Klaus Arthur Schmid, Nikos Mamoulis, Arthur Zimek, Mathias Renz
Representative clustering of uncertain data
In: Proc. 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
DOI:10.1145/2623330.2623725
DBLP:conf/kdd/ZufleESMZR14

de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.UKMeans
M. Chau, R. Cheng, B. Kao, J. Ng
Uncertain data mining: An example in clustering location data
In: Proc. 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006)
DOI:10.1007/11731139_24
DBLP:conf/pakdd/ChauCKN06

de.lmu.ifi.dbs.elki.algorithm.itemsetmining.APRIORI
R. Agrawal, R. Srikant
Fast Algorithms for Mining Association Rules
In: Proc. 20th Int. Conf. on Very Large Data Bases (VLDB ‘94)
Online: http://www.vldb.org/conf/1994/P487.PDF
DBLP:conf/vldb/AgrawalS94

de.lmu.ifi.dbs.elki.algorithm.itemsetmining.Eclat
M. J. Zaki, S. Parthasarathy, M. Ogihara, W. Li
New Algorithms for Fast Discovery of Association Rules
In: Proc. 3rd ACM SIGKDD ‘97 Int. Conf. on Knowledge Discovery and Data Mining
Online: http://www.aaai.org/Library/KDD/1997/kdd97-060.php
DBLP:conf/kdd/ZakiPOL97

de.lmu.ifi.dbs.elki.algorithm.itemsetmining.FPGrowth
J. Han, J. Pei, Y. Yin
Mining frequent patterns without candidate generation
In: Proc. ACM SIGMOD Int. Conf. Management of Data (SIGMOD 2000)
DOI:10.1145/342009.335372
DBLP:conf/sigmod/HanPY00

de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules.AssociationRuleGeneration
M. J. Zaki, W. Meira Jr.
Data mining and analysis: fundamental concepts and algorithms
In: Cambridge University Press, 2014
DBLP:books/cu/ZM2014

de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules.interest.AddedValue
S. Sahar, Sigal, Y. Mansour
Empirical evaluation of interest-level criteria
In: Proc. SPIE 3695, Data Mining and Knowledge Discovery: Theory, Tools, and Technology
DOI:10.1117/12.339991
DBLP:conf/dmkdttt/SaharM99

de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules.interest.CertaintyFactor
F. Berzal, I. Blanco, D. Sánchez, M. Vila
Measuring the accuracy and interest of association rules: A new framework
In: Intelligent Data Analysis, 6(3), 2002
Online: http://content.iospress.com/articles/intelligent-data-analysis/ida00089
DBLP:journals/ida/GalianoBSM02

de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules.interest.Confidence
R. Agrawal, T. Imielinski, A. Swami
Mining association rules between sets of items in large databases
In: Proc. ACM SIGMOD International Conference on Management of Data
DOI:10.1145/170036.170072
DBLP:conf/sigmod/AgrawalIS93

de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules.interest.Conviction
S. Brin, R. Motwani, J. D Ullman, S Tsur
Dynamic itemset counting and implication rules for market basket data
In: Proc. 1997 ACM SIGMOD international conference on management of data
DOI:10.1145/253260.253325
DBLP:conf/sigmod/BrinMUT97

de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules.interest.Cosine,
de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules.interest.GiniIndex
P. Tan, V. Kumar
Interestingness measures for association patterns: A perspective
In: Proc. Workshop on Postprocessing in Machine Learning and Data Mining
Online: https://www.cs.umn.edu/sites/cs.umn.edu/files/tech_reports/00-036.pdf

de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules.interest.GiniIndex
L. Breiman, J. H. Friedman, R. A. Olshen, C. J. Stone
Classification and Regression Trees

de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules.interest.JMeasure
R. M. Goodman, P. Smyth
Rule induction using information theory
In: Knowledge Discovery in Databases 1991
DBLP:books/mit/PF91/SmythG91

de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules.interest.Jaccard
C. J. van Rijsbergen
Information Retrieval, 2nd Edition
In: Butterworths, London, 1979
DBLP:books/bu/Rijsbergen79

de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules.interest.Jaccard
P.-N. Tan, V. Kumar, J. Srivastava
Selecting the right objective measure for association analysis
In: Information Systems 29.4
DOI:10.1016/S0306-4379(03)00072-3
DBLP:journals/is/TanKS04

de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules.interest.Klosgen
W. Klösgen
Explora: A multipattern and multistrategy discovery assistant
In: Advances in Knowledge Discovery and Data Mining
DBLP:books/mit/fayyadPSU96/Klosgen96

de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules.interest.Leverage
G. Piatetsky-Shapiro
Discovery, analysis, and presentation of strong rules
In: Knowledge Discovery in Databases 1991
DBLP:books/mit/PF91/Piatetsky91

de.lmu.ifi.dbs.elki.algorithm.itemsetmining.associationrules.interest.Lift
S. Brin, R. Motwani, C. Silverstein
Beyond market baskets: Generalizing association rules to correlations
In: ACM SIGMOD Record 26
DOI:10.1145/253260.253327
DBLP:conf/sigmod/BrinMS97

de.lmu.ifi.dbs.elki.algorithm.outlier.COP,
de.lmu.ifi.dbs.elki.math.linearalgebra.pca.RANSACCovarianceMatrixBuilder,
de.lmu.ifi.dbs.elki.utilities.scaling.outlier.COPOutlierScaling,
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.outlier.COPVectorVisualization
Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek
Outlier Detection in Arbitrarily Oriented Subspaces
In: Proc. IEEE Int. Conf. on Data Mining (ICDM 2012)
DOI:10.1109/ICDM.2012.21
DBLP:conf/icdm/KriegelKSZ12

de.lmu.ifi.dbs.elki.algorithm.outlier.DWOF
R. Momtaz, N. Mohssen, M. A. Gowayyed
DWOF: A Robust Density-Based Outlier Detection Approach
In: Proc. 6th Iberian Conf. Pattern Recognition and Image Analysis (IbPRIA 2013)
DOI:10.1007/978-3-642-38628-2_61
DBLP:conf/ibpria/MomtazMG13

de.lmu.ifi.dbs.elki.algorithm.outlier.GaussianUniformMixture
Generalization using the likelihood gain as outlier score of
E. Eskin
Anomaly detection over noisy data using learned probability distributions
In: Proc. 17th Int. Conf. on Machine Learning (ICML-2000)
DOI:10.7916/D8C53SKF
DBLP:conf/icml/Eskin00

de.lmu.ifi.dbs.elki.algorithm.outlier.OPTICSOF
Markus M. Breunig, Hans-Peter Kriegel, Raymond Ng, Jörg Sander
OPTICS-OF: Identifying Local Outliers
In: Proc. 3rd European Conf. on Principles of Knowledge Discovery and Data Mining (PKDD’99)
DOI:10.1007/978-3-540-48247-5_28
DBLP:conf/pkdd/BreunigKNS99

de.lmu.ifi.dbs.elki.algorithm.outlier.SimpleCOP
Arthur Zimek
Application 2: Outlier Detection (Chapter 18)
In: Correlation Clustering

de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased.ABOD,
de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased.FastABOD,
de.lmu.ifi.dbs.elki.algorithm.outlier.anglebased.LBABOD
Hans-Peter Kriegel, Matthias Schubert, Arthur Zimek
Angle-Based Outlier Detection in High-dimensional Data
In: Proc. 14th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD’08)
DOI:10.1145/1401890.1401946
DBLP:conf/kdd/KriegelSZ08

de.lmu.ifi.dbs.elki.algorithm.outlier.clustering.CBLOF
Z. He, X. Xu, S. Deng
Discovering cluster-based local outliers
In: Pattern Recognition Letters 24(9-10)
DOI:10.1016/S0167-8655(03)00003-5
DBLP:journals/prl/HeXD03

de.lmu.ifi.dbs.elki.algorithm.outlier.clustering.SilhouetteOutlierDetection,
de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateSilhouette
P. J. Rousseeuw
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
In: Journal of Computational and Applied Mathematics, Volume 20
DOI:10.1016/0377-0427(87)90125-7

de.lmu.ifi.dbs.elki.algorithm.outlier.distance.AbstractDBOutlier,
de.lmu.ifi.dbs.elki.algorithm.outlier.distance.DBOutlierDetection
E. M. Knorr, R. T. Ng
Algorithms for Mining Distance-Based Outliers in Large Datasets
In: Proc. Int. Conf. on Very Large Databases (VLDB’98)
Online: http://www.vldb.org/conf/1998/p392.pdf
DBLP:conf/vldb/KnorrN98

de.lmu.ifi.dbs.elki.algorithm.outlier.distance.DBOutlierScore
Generalization of a method proposed in
E. M. Knorr, R. T. Ng
Algorithms for Mining Distance-Based Outliers in Large Datasets
In: Proc. Int. Conf. on Very Large Databases (VLDB’98)
Online: http://www.vldb.org/conf/1998/p392.pdf
DBLP:conf/vldb/KnorrN98

de.lmu.ifi.dbs.elki.algorithm.outlier.distance.HilOut,
de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNWeightOutlier
F. Angiulli, C. Pizzuti
Fast Outlier Detection in High Dimensional Spaces
In: Proc. European Conf. Principles of Knowledge Discovery and Data Mining (PKDD’02)
DOI:10.1007/3-540-45681-3_2
DBLP:conf/pkdd/AngiulliP02

de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNDD
D. de Ridder, D. M. J. Tax, R. P. W. Duin
An experimental comparison of one-class classification methods
In: Proc. 4th Ann. Conf. Advanced School for Computing and Imaging (ASCI’98)
Online: http://prlab.tudelft.nl/sites/default/files/asci_98.pdf

de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNOutlier
S. Ramaswamy, R. Rastogi, K. Shim
Efficient Algorithms for Mining Outliers from Large Data Sets
In: Proc. Int. Conf. on Management of Data (SIGMOD 2000)
DOI:10.1145/342009.335437
DBLP:conf/sigmod/RamaswamyRS00

de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNSOS,
de.lmu.ifi.dbs.elki.algorithm.outlier.distance.SOS,
de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.ISOS
Erich Schubert, Michael Gertz
Intrinsic t-Stochastic Neighbor Embedding for Visualization and Outlier Detection: A Remedy Against the Curse of Dimensionality?
In: Proc. Int. Conf. Similarity Search and Applications, SISAP’2017
DOI:10.1007/978-3-319-68474-1_13
DBLP:conf/sisap/SchubertG17

de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNSOS,
de.lmu.ifi.dbs.elki.algorithm.outlier.distance.SOS
J. Janssens, F. Huszár, E. Postma, J. van den Herik
Stochastic Outlier Selection
In: TiCC TR 2012–001
Online: https://www.tilburguniversity.edu/upload/b7bac5b2-9b00-402a-9261-7849aa019fbb_sostr.pdf

de.lmu.ifi.dbs.elki.algorithm.outlier.distance.LocalIsolationCoefficient
B. Yu, M. Song, L. Wang
Local Isolation Coefficient-Based Outlier Mining Algorithm
In: Int. Conf. on Information Technology and Computer Science (ITCS) 2009
DOI:10.1109/ITCS.2009.230

de.lmu.ifi.dbs.elki.algorithm.outlier.distance.ODIN,
tutorial.outlier.ODIN
V. Hautamäki, I. Kärkkäinen, P. Fränti
Outlier detection using k-nearest neighbour graph
In: Proc. 17th Int. Conf. Pattern Recognition (ICPR 2004)
DOI:10.1109/ICPR.2004.1334558
DBLP:conf/icpr/HautamakiKF04

de.lmu.ifi.dbs.elki.algorithm.outlier.distance.ReferenceBasedOutlierDetection
Y. Pei, O. R. Zaiane, Y. Gao
An Efficient Reference-based Approach to Outlier Detection in Large Datasets
In: Proc. 6th IEEE Int. Conf. on Data Mining (ICDM ‘06)
DOI:10.1109/ICDM.2006.17
DBLP:conf/icdm/PeiZG06

de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel.ParallelKNNOutlier,
de.lmu.ifi.dbs.elki.algorithm.outlier.distance.parallel.ParallelKNNWeightOutlier,
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.SimplifiedLOF,
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel,
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel.ParallelLOF,
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.parallel.ParallelSimplifiedLOF
Erich Schubert, Arthur Zimek, Hans-Peter Kriegel
Local Outlier Detection Reconsidered: a Generalized View on Locality with Applications to Spatial, Video, and Network Outlier Detection
In: Data Mining and Knowledge Discovery 28(1)
DOI:10.1007/s10618-012-0300-z
DBLP:journals/datamine/SchubertZK14

de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.IDOS
Jonathan von Brünken, Michael E. Houle, Arthur Zimek
Intrinsic Dimensional Outlier Detection in High-Dimensional Data
In: NII Technical Report (NII-2015-003E)
Online: http://www.nii.ac.jp/TechReports/15-003E.html

de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.IntrinsicDimensionalityOutlier
Michael E. Houle, Erich Schubert, Arthur Zimek
On the Correlation Between Local Intrinsic Dimensionality and Outlierness
In: Proc. 11th Int. Conf. Similarity Search and Applications (SISAP’2018)
DBLP:conf/sisap/HouleSZ18

de.lmu.ifi.dbs.elki.algorithm.outlier.lof.ALOCI,
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LOCI
S. Papadimitriou, H. Kitagawa, P. B. Gibbons, C. Faloutsos
LOCI: Fast Outlier Detection Using the Local Correlation Integral
In: Proc. 19th IEEE Int. Conf. on Data Engineering (ICDE ‘03)
DOI:10.1109/ICDE.2003.1260802
DBLP:conf/icde/PapadimitriouKGF03

de.lmu.ifi.dbs.elki.algorithm.outlier.lof.COF
J. Tang, Z. Chen, A. W. C. Fu, D. W. Cheung
Enhancing effectiveness of outlier detections for low density patterns
In: In Advances in Knowledge Discovery and Data Mining
DOI:10.1007/3-540-47887-6_53
DBLP:conf/pakdd/TangCFC02

de.lmu.ifi.dbs.elki.algorithm.outlier.lof.FlexibleLOF,
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LOF
Markus M. Breunig, Hans-Peter Kriegel, Raymond Ng, Jörg Sander
LOF: Identifying Density-Based Local Outliers
In: Proc. 2nd ACM SIGMOD Int. Conf. on Management of Data (SIGMOD’00)
DOI:10.1145/342009.335388
DBLP:conf/sigmod/BreunigKNS00

de.lmu.ifi.dbs.elki.algorithm.outlier.lof.INFLO
W. Jin, A. Tung, J. Han, W. Wang
Ranking outliers using symmetric neighborhood relationship
In: Proc. 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
DOI:10.1007/11731139_68
DBLP:conf/pakdd/JinTHW06

de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS
Erich Schubert, Arthur Zimek, Hans-Peter Kriegel
Generalized Outlier Detection with Flexible Kernel Density Estimates
In: Proc. 14th SIAM International Conference on Data Mining (SDM 2014)
DOI:10.1137/1.9781611973440.63
DBLP:conf/sdm/SchubertZK14

de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDF
L. J. Latecki, A. Lazarevic, D. Pokrajac
Outlier Detection with Kernel Density Functions
In: Machine Learning and Data Mining in Pattern Recognition
DOI:10.1007/978-3-540-73499-4_6
DBLP:conf/mldm/LateckiLP07

de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDOF
K. Zhang, M. Hutter, H. Jin
A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data
In: Proc. 13th Pacific-Asia Conf. Adv. Knowledge Discovery and Data Mining (PAKDD 2009)
DOI:10.1007/978-3-642-01307-2_84
DBLP:conf/pakdd/ZhangHJ09

de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LoOP
Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek
LoOP: Local Outlier Probabilities
In: Proc. 18th Int. Conf. Information and Knowledge Management (CIKM 2009)
DOI:10.1145/1645953.1646195
DBLP:conf/cikm/KriegelKSZ09

de.lmu.ifi.dbs.elki.algorithm.outlier.lof.VarianceOfVolume
T. Hu, S. Y. Sung
Detecting pattern-based outliers
In: Pattern Recognition Letters 24(16)
DOI:10.1016/S0167-8655(03)00165-X
DBLP:journals/prl/HuS03

de.lmu.ifi.dbs.elki.algorithm.outlier.meta.FeatureBagging
A. Lazarevic, V. Kumar
Feature Bagging for Outlier Detection
In: Proc. 11th ACM SIGKDD Int. Conf. on Knowledge Discovery in Data Mining
DOI:10.1145/1081870.1081891
DBLP:conf/kdd/LazarevicK05

de.lmu.ifi.dbs.elki.algorithm.outlier.meta.HiCS,
de.lmu.ifi.dbs.elki.math.statistics.dependence.HiCSDependenceMeasure
F. Keller, E. Müller, K. Böhm
HiCS: High Contrast Subspaces for Density-Based Outlier Ranking
In: Proc. IEEE 28th Int. Conf. on Data Engineering (ICDE 2012)
DOI:10.1109/ICDE.2012.88
DBLP:conf/icde/KellerMB12

de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuGLSBackwardSearchAlgorithm
F. Chen, C.-T. Lu, A. P. Boedihardjo
GLS-SOD: A Generalized Local Statistical Approach for Spatial Outlier Detection
In: Proc. 16th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining
DOI:10.1145/1835804.1835939
DBLP:conf/kdd/ChenLB10

de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuMeanMultipleAttributes,
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuMedianMultipleAttributes
C.-T. Lu, D. Chen, Y. Kou
Detecting Spatial Outliers with Multiple Attributes
In: Proc. 15th IEEE Int. Conf. Tools with Artificial Intelligence (TAI 2003)
DOI:10.1109/TAI.2003.1250179
DBLP:conf/ictai/LuCK03

de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuMedianAlgorithm
C.-T. Lu, D. Chen, Y. Kou
Algorithms for Spatial Outlier Detection
In: Proc. 3rd IEEE International Conference on Data Mining
DOI:10.1109/ICDM.2003.1250986
DBLP:conf/icdm/LuCK03

de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuMoranScatterplotOutlier,
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuScatterplotOutlier,
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuZTestOutlier
S. Shekhar, C.-T. Lu, P. Zhang
A Unified Approach to Detecting Spatial Outliers
In: GeoInformatica 7-2, 2003
DOI:10.1023/A:1023455925009
DBLP:journals/geoinformatica/ShekharLZ03

de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuRandomWalkEC
X. Liu, C.-T. Lu, F. Chen
Spatial outlier detection: random walk based approaches
In: Proc. SIGSPATIAL Int. Conf. Advances in Geographic Information Systems
DOI:10.1145/1869790.1869841
DBLP:conf/gis/LiuLC10

de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.SLOM
S. Chawla, P. Sun
SLOM: a new measure for local spatial outliers
In: Knowledge and Information Systems 9(4)
DOI:10.1007/s10115-005-0200-2
DBLP:journals/kais/ChawlaS06

de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.SOF
T. Huang, X. Qin
Detecting outliers in spatial database
In: Proc. 3rd International Conference on Image and Graphics
DOI:10.1109/ICIG.2004.53
DBLP:conf/icig/HuangQ04

de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.TrimmedMeanApproach
T. Hu, S. Y. Sung
A trimmed mean approach to finding spatial outliers
In: Intelligent Data Analysis 8
Online: http://content.iospress.com/articles/intelligent-data-analysis/ida00153
DBLP:journals/ida/HuS04

de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.AbstractAggarwalYuOutlier,
de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.AggarwalYuEvolutionary,
de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.AggarwalYuNaive
C. C. Aggarwal, P. S. Yu
Outlier detection for high dimensional data
In: Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD 2001)
DOI:10.1145/375663.375668
DBLP:conf/sigmod/AggarwalY01

de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.OUTRES
E. Müller, M. Schiffer, T. Seidl
Adaptive outlierness for subspace outlier ranking
In: Proc. 19th ACM Int. Conf. on Information and Knowledge Management
DOI:10.1145/1871437.1871690
DBLP:conf/cikm/MullerSS10

de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.OutRankS1
Emmanuel Müller, Ira Assent, Uwe Steinhausen, Thomas Seidl
OutRank: ranking outliers in high dimensional data
In: Proc. 24th Int. Conf. on Data Engineering (ICDE) Workshop on Ranking in Databases (DBRank)
DOI:10.1109/ICDEW.2008.4498387
DBLP:conf/icde/MullerASS08

de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.SOD
Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek
Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data
In: Proc. Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD 2009)
DOI:10.1007/978-3-642-01307-2_86
DBLP:conf/pakdd/KriegelKSZ09

de.lmu.ifi.dbs.elki.algorithm.outlier.svm.LibSVMOneClassOutlierDetection
B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, R. C. Williamson
Estimating the support of a high-dimensional distribution
In: Neural computation 13.7
DOI:10.1162/089976601750264965
DBLP:journals/neco/ScholkopfPSSW01

de.lmu.ifi.dbs.elki.algorithm.projection.BarnesHutTSNE,
de.lmu.ifi.dbs.elki.algorithm.projection.NearestNeighborAffinityMatrixBuilder
L. J. P. van der Maaten
Accelerating t-SNE using Tree-Based Algorithms
In: Journal of Machine Learning Research 15
Online: http://dl.acm.org/citation.cfm?id=2697068
DBLP:journals/jmlr/Maaten14

de.lmu.ifi.dbs.elki.algorithm.projection.GaussianAffinityMatrixBuilder,
de.lmu.ifi.dbs.elki.algorithm.projection.PerplexityAffinityMatrixBuilder,
de.lmu.ifi.dbs.elki.algorithm.projection.SNE
G. Hinton, S. Roweis
Stochastic Neighbor Embedding
In: Advances in Neural Information Processing Systems 15
Online: http://papers.nips.cc/paper/2276-stochastic-neighbor-embedding
DBLP:conf/nips/HintonR02

de.lmu.ifi.dbs.elki.algorithm.projection.IntrinsicNearestNeighborAffinityMatrixBuilder
Erich Schubert, Michael Gertz
Intrinsic t-Stochastic Neighbor Embedding for Visualization and Outlier Detection: A Remedy Against the Curse of Dimensionality?
In: Proc. Int. Conf. Similarity Search and Applications, SISAP 2017
DOI:10.1007/978-3-319-68474-1_13
DBLP:conf/sisap/SchubertG17

de.lmu.ifi.dbs.elki.algorithm.projection.TSNE
L. J. P. van der Maaten, G. E. Hinton
Visualizing High-Dimensional Data Using t-SNE
In: Journal of Machine Learning Research 9
Online: http://www.jmlr.org/papers/v9/vandermaaten08a.html

de.lmu.ifi.dbs.elki.algorithm.statistics.HopkinsStatisticClusteringTendency
B. Hopkins, J. G. Skellam
A new method for determining the type of distribution of plant individuals
In: Annals of Botany, 18(2), 213-227
DOI:10.1093/oxfordjournals.aob.a083391

de.lmu.ifi.dbs.elki.algorithm.timeseries.OfflineChangePointDetectionAlgorithm
E. S. Page
On Problems in which a Change in a Parameter Occurs at an Unknown Point
In: Biometrika Vol. 44
DOI:10.2307/2333258

de.lmu.ifi.dbs.elki.algorithm.timeseries.OfflineChangePointDetectionAlgorithm
M. Basseville, I. V. Nikiforov
Section 2.6: Off-line Change Detection
In: Detection of Abrupt Changes - Theory and Application
Online: http://people.irisa.fr/Michele.Basseville/kniga/kniga.pdf

de.lmu.ifi.dbs.elki.algorithm.timeseries.OfflineChangePointDetectionAlgorithm
D. Picard
**Testing and Estimating Change-Points in Time Series **
In: Advances in Applied Probability Vol. 17
DOI:10.2307/1427090

de.lmu.ifi.dbs.elki.algorithm.timeseries.SigniTrendChangeDetection
Erich Schubert, Michael Weiler, Hans-Peter Kriegel
Signi-Trend: scalable detection of emerging topics in textual streams by hashed significance thresholds
In: Proc. 20th ACM SIGKDD international conference on Knowledge discovery and data mining
DOI:10.1145/2623330.2623740
DBLP:conf/kdd/SchubertWK14

de.lmu.ifi.dbs.elki.application.AbstractApplication
Erich Schubert and Arthur Zimek
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 “Heidelberg”
In: CoRR
Online: https://arxiv.org/abs/1902.03616
DBLP:journals/corr/abs-1902-03616

de.lmu.ifi.dbs.elki.application.experiments.VisualizeGeodesicDistances,
de.lmu.ifi.dbs.elki.distance.distancefunction.geo.DimensionSelectingLatLngDistanceFunction,
de.lmu.ifi.dbs.elki.distance.distancefunction.geo.LatLngDistanceFunction,
de.lmu.ifi.dbs.elki.distance.distancefunction.geo.LngLatDistanceFunction,
de.lmu.ifi.dbs.elki.math.geodesy.SphereUtil
Erich Schubert, Arthur Zimek, Hans-Peter Kriegel
Geodetic Distance Queries on R-Trees for Indexing Geographic Data
In: Int. Symp. Advances in Spatial and Temporal Databases (SSTD’2013)
DOI:10.1007/978-3-642-40235-7_9
DBLP:conf/ssd/SchubertZK13

de.lmu.ifi.dbs.elki.application.greedyensemble.ComputeKNNOutlierScores,
de.lmu.ifi.dbs.elki.application.greedyensemble.GreedyEnsembleExperiment,
de.lmu.ifi.dbs.elki.application.greedyensemble.VisualizePairwiseGainMatrix
Erich Schubert, Remigius Wojdanowski, Arthur Zimek, Hans-Peter Kriegel
On Evaluation of Outlier Rankings and Outlier Scores
In: Proc. 12th SIAM Int. Conf. on Data Mining (SDM 2012)
DOI:10.1137/1.9781611972825.90
DBLP:conf/sdm/SchubertWZK12

de.lmu.ifi.dbs.elki.data.projection.random.AchlioptasRandomProjectionFamily
D. Achlioptas
Database-friendly random projections: Johnson-Lindenstrauss with binary coins
In: Proc. 20th ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
DOI:10.1145/375551.375608
DBLP:conf/pods/Achlioptas01

de.lmu.ifi.dbs.elki.data.projection.random.CauchyRandomProjectionFamily,
de.lmu.ifi.dbs.elki.data.projection.random.GaussianRandomProjectionFamily,
de.lmu.ifi.dbs.elki.index.lsh.hashfamilies.EuclideanHashFunctionFamily,
de.lmu.ifi.dbs.elki.index.lsh.hashfamilies.ManhattanHashFunctionFamily,
de.lmu.ifi.dbs.elki.index.lsh.hashfunctions.MultipleProjectionsLocalitySensitiveHashFunction
M. Datar, N. Immorlica, P. Indyk, V. S. Mirrokni
Locality-sensitive hashing scheme based on p-stable distributions
In: Proc. 20th Annual Symposium on Computational Geometry
DOI:10.1145/997817.997857
DBLP:conf/compgeom/DatarIIM04

de.lmu.ifi.dbs.elki.data.projection.random.RandomSubsetProjectionFamily
L. Breiman
Bagging predictors
In: Machine learning 24.2
DOI:10.1007/BF00058655
DBLP:journals/ml/Breiman96b

de.lmu.ifi.dbs.elki.data.projection.random.SimplifiedRandomHyperplaneProjectionFamily,
de.lmu.ifi.dbs.elki.index.lsh.hashfamilies.CosineHashFunctionFamily
M. Henzinger
Finding near-duplicate web pages: a large-scale evaluation of algorithms
In: Proc. 29th ACM Conf. Research and Development in Information Retrieval (SIGIR 2006)
DOI:10.1145/1148170.1148222
DBLP:conf/sigir/Henzinger06

de.lmu.ifi.dbs.elki.data.uncertain.UnweightedDiscreteUncertainObject,
de.lmu.ifi.dbs.elki.data.uncertain.WeightedDiscreteUncertainObject
N. Dalvi, C. Ré, D. Suciu
Probabilistic databases: diamonds in the dirt
In: Communications of the ACM 52, 7
DOI:10.1145/1538788.1538810
DBLP:journals/cacm/DalviRS09

de.lmu.ifi.dbs.elki.data.uncertain.UnweightedDiscreteUncertainObject,
de.lmu.ifi.dbs.elki.data.uncertain.WeightedDiscreteUncertainObject
O. Benjelloun, A. D. Sarma, A. Halevy, J. Widom
ULDBs: Databases with uncertainty and lineage
In: Proc. of the 32nd Int. Conf. on Very Large Data Bases (VLDB)
Online: http://www.vldb.org/conf/2006/p953-benjelloun.pdf
DBLP:conf/vldb/BenjellounSHW06

de.lmu.ifi.dbs.elki.data.uncertain.WeightedDiscreteUncertainObject
Thomas Bernecker, Hans-Peter Kriegel, Matthias Renz, Florian Verhein, Andreas Züfle
Probabilistic frequent itemset mining in uncertain databases
In: Proc. 15th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining
DOI:10.1145/1557019.1557039
DBLP:conf/kdd/BerneckerKRVZ09

de.lmu.ifi.dbs.elki.database.ids.integer.IntegerDBIDArrayQuickSort,
de.lmu.ifi.dbs.elki.utilities.datastructures.arrays.IntegerArrayQuickSort
V. Yaroslavskiy
Dual-Pivot Quicksort
Online: http://iaroslavski.narod.ru/quicksort/

de.lmu.ifi.dbs.elki.datasource.filter.transform.LinearDiscriminantAnalysisFilter
R. A. Fisher
The use of multiple measurements in taxonomic problems
In: Annals of Eugenics 7.2
DOI:10.1111/j.1469-1809.1936.tb02137.x

de.lmu.ifi.dbs.elki.datasource.filter.transform.PerturbationFilter
A. Zimek, R. J. G. B. Campello, J. Sander
Data Perturbation for Outlier Detection Ensembles
In: Proc. 26th International Conference on Scientific and Statistical Database Management (SSDBM), Aalborg, Denmark, 2014
DOI:10.1145/2618243.2618257
DBLP:conf/ssdbm/ZimekCS14

de.lmu.ifi.dbs.elki.distance.distancefunction.BrayCurtisDistanceFunction
T. Sørensen
A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons
In: Kongelige Danske Videnskabernes Selskab 5 (4)

de.lmu.ifi.dbs.elki.distance.distancefunction.BrayCurtisDistanceFunction
J. R. Bray, J. T. Curtis
An ordination of the upland forest communities of southern Wisconsin
In: Ecological monographs 27.4
DOI:10.2307/1942268

de.lmu.ifi.dbs.elki.distance.distancefunction.BrayCurtisDistanceFunction
L. R. Dice
Measures of the Amount of Ecologic Association Between Species
In: Ecology 26 (3)
DOI:10.2307/1932409

de.lmu.ifi.dbs.elki.distance.distancefunction.CanberraDistanceFunction
G. N. Lance, W. T. Williams
Computer Programs for Hierarchical Polythetic Classification (Similarity Analyses)
In: Computer Journal, Volume 9, Issue 1
DOI:10.1093/comjnl/9.1.60

de.lmu.ifi.dbs.elki.distance.distancefunction.ClarkDistanceFunction,
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.FisherRaoDistanceFunction,
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.HellingerDistanceFunction,
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.JensenShannonDivergenceDistanceFunction,
de.lmu.ifi.dbs.elki.distance.similarityfunction.Kulczynski1SimilarityFunction,
de.lmu.ifi.dbs.elki.distance.similarityfunction.Kulczynski2SimilarityFunction
M.-M. Deza, E. Deza
Dictionary of distances
In: Dictionary of distances
DOI:10.1007/978-3-642-00234-2

de.lmu.ifi.dbs.elki.distance.distancefunction.MahalanobisDistanceFunction,
de.lmu.ifi.dbs.elki.math.linearalgebra.VMath
P. C. Mahalanobis
On the generalized distance in statistics
In: Proceedings of the National Institute of Sciences of India. 2 (1)

de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram.HSBHistogramQuadraticDistanceFunction
J. R. Smith, S. F. Chang
VisualSEEk: a fully automated content-based image query system
In: Proc. 4th ACM Int. Conf. on Multimedia 1997
DOI:10.1145/244130.244151
DBLP:conf/mm/SmithC96

de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram.HistogramIntersectionDistanceFunction
M. J. Swain, D. H. Ballard
Color Indexing
In: International Journal of Computer Vision, 7(1), 32, 1991
DOI:10.1007/BF00130487
DBLP:journals/ijcv/SwainB91

de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram.RGBHistogramQuadraticDistanceFunction
J. Hafner, H. S. Sawhney, W. Equits, M. Flickner, W. Niblack
Efficient Color Histogram Indexing for Quadratic Form Distance Functions
In: IEEE Trans. on Pattern Analysis and Machine Intelligence 17(7)
DOI:10.1109/34.391417
DBLP:journals/pami/HafnerSEFN95

de.lmu.ifi.dbs.elki.distance.distancefunction.histogram.HistogramMatchDistanceFunction
L. N. Vaserstein
Markov processes over denumerable products of spaces describing large systems of automata
In: Problemy Peredachi Informatsii 5.3 / Problems of Information Transmission, 5:3
Online: http://mi.mathnet.ru/eng/ppi1811

de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.ChiDistanceFunction,
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.SqrtJensenShannonDivergenceDistanceFunction
D. M. Endres, J. E. Schindelin
A new metric for probability distributions
In: IEEE Transactions on Information Theory, 49(7)
DOI:10.1109/TIT.2003.813506
DBLP:journals/tit/EndresS03

de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.ChiDistanceFunction,
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.ChiSquaredDistanceFunction,
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.JeffreyDivergenceDistanceFunction
J. Puzicha, J. M. Buhmann, Y. Rubner, C. Tomasi
Empirical evaluation of dissimilarity measures for color and texture
In: Proc. 7th IEEE International Conference on Computer Vision
DOI:10.1109/ICCV.1999.790412
DBLP:conf/iccv/PuzichaRTB99

de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.FisherRaoDistanceFunction
C. R. Rao
Information and the Accuracy Attainable in the Estimation of Statistical Parameters
In: Bulletin of the Calcutta Mathematical Society 37(3)

de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.HellingerDistanceFunction
E. Hellinger
Neue Begründung der Theorie quadratischer Formen von unendlichvielen Veränderlichen
In: Journal für die reine und angewandte Mathematik
Online: http://resolver.sub.uni-goettingen.de/purl?GDZPPN002166941

de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.JeffreyDivergenceDistanceFunction,
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.JensenShannonDivergenceDistanceFunction
D. M. Endres, J. E. Schindelin
A new metric for probability distributions
In: IEEE Transactions on Information Theory 49(7)
DOI:10.1109/TIT.2003.813506
DBLP:journals/tit/EndresS03

de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.JeffreyDivergenceDistanceFunction
H. Jeffreys
An invariant form for the prior probability in estimation problems
In: Proc. Royal Society A: Mathematical, Physical and Engineering Sciences 186(1007)
DOI:10.1098/rspa.1946.0056

de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.JeffreyDivergenceDistanceFunction,
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.TriangularDiscriminationDistanceFunction
F. Topsøe
Some inequalities for information divergence and related measures of discrimination
In: IEEE Transactions on information theory, 46(4)
DOI:10.1109/18.850703
DBLP:journals/tit/Topsoe00

de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.JensenShannonDivergenceDistanceFunction
J. Lin
Divergence measures based on the Shannon entropy
In: IEEE Transactions on Information Theory 37(1)
DOI:10.1109/18.61115
DBLP:journals/tit/Lin91

de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.KullbackLeiblerDivergenceAsymmetricDistanceFunction,
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction
S. Kullback
Information theory and statistics

de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.TriangularDistanceFunction
R. Connor, F. A. Cardillo, L. Vadicamo, F. Rabitti
Hilbert Exclusion: Improved Metric Search through Finite Isometric Embeddings
In: arXiv preprint arXiv:1604.08640
Online: http://arxiv.org/abs/1604.08640
DBLP:journals/corr/ConnorCVR16

de.lmu.ifi.dbs.elki.distance.distancefunction.set.HammingDistanceFunction
R. W. Hamming
Error detecting and error correcting codes
In: Bell System technical journal, 29(2)
DOI:10.1002/j.1538-7305.1950.tb00463.x

de.lmu.ifi.dbs.elki.distance.distancefunction.set.JaccardSimilarityDistanceFunction,
de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster.ClusterJaccardSimilarityFunction,
de.lmu.ifi.dbs.elki.evaluation.clustering.PairCounting
P. Jaccard
Distribution de la florine alpine dans la Bassin de Dranses et dans quelques regiones voisines
In: Bulletin del la Société Vaudoise des Sciences Naturelles
Online: http://data.rero.ch/01-R241574160

de.lmu.ifi.dbs.elki.distance.distancefunction.strings.LevenshteinDistanceFunction,
de.lmu.ifi.dbs.elki.distance.distancefunction.strings.NormalizedLevenshteinDistanceFunction
V. I. Levenshtein
Binary codes capable of correcting deletions, insertions and reversals
In: Soviet physics doklady 10

de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries.DTWDistanceFunction
D. Berndt, J. Clifford
Using dynamic time warping to find patterns in time series
In: AAAI-94 Workshop on Knowledge Discovery in Databases, 1994
Online: http://www.aaai.org/Papers/Workshops/1994/WS-94-03/WS94-03-031.pdf
DBLP:conf/kdd/BerndtC94

de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries.DerivativeDTWDistanceFunction
E. J. Keogh, M. J. Pazzani
Derivative dynamic time warping
In: 1st SIAM Int. Conf. on Data Mining (SDM-2001)
DOI:10.1137/1.9781611972719.1
DBLP:conf/sdm/KeoghP01

de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries.EDRDistanceFunction
L. Chen, M. T. Özsu, V. Oria
Robust and fast similarity search for moving object trajectories
In: Proc. 2005 ACM SIGMOD Int. Conf. Management of Data
DOI:10.1145/1066157.1066213
DBLP:conf/sigmod/ChenOO05

de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries.ERPDistanceFunction
L. Chen, R. Ng
On the marriage of Lp-norms and edit distance
In: Proc. 13th Int. Conf. on Very Large Data Bases (VLDB ‘04)
Online: http://www.vldb.org/conf/2004/RS21P2.PDF
DBLP:conf/vldb/ChenN04

de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries.LCSSDistanceFunction
M. Vlachos, M. Hadjieleftheriou, D. Gunopulos, E. Keogh
Indexing Multi-Dimensional Time-Series with Support for Multiple Distance Measures
In: Proc. 9th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining
DOI:10.1145/956750.956777
DBLP:conf/kdd/VlachosHGK03

de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster.ClusteringAdjustedRandIndexSimilarityFunction,
de.lmu.ifi.dbs.elki.evaluation.clustering.PairCounting
L. Hubert, P. Arabie
Comparing partitions
In: Journal of Classification 2(193)
DOI:10.1007/BF01908075

de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster.ClusteringBCubedF1SimilarityFunction,
de.lmu.ifi.dbs.elki.evaluation.clustering.BCubed
A. Bagga, B. Baldwin
Entity-based cross-document coreferencing using the Vector Space Model
In: Proc. 17th Int. Conf. on Computational Linguistics (COLING ‘98)
DOI:10.3115/980451.980859

de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster.ClusteringFowlkesMallowsSimilarityFunction,
de.lmu.ifi.dbs.elki.evaluation.clustering.PairCounting
E. B. Fowlkes, C. L. Mallows
A method for comparing two hierarchical clusterings
In: Journal of the American Statistical Association, Vol. 78 Issue 383
DOI:10.2307/2288117

de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster.ClusteringRandIndexSimilarityFunction,
de.lmu.ifi.dbs.elki.evaluation.clustering.PairCounting
W. M. Rand
Objective Criteria for the Evaluation of Clustering Methods
In: Journal of the American Statistical Association, Vol. 66 Issue 336
DOI:10.2307/2284239

de.lmu.ifi.dbs.elki.evaluation.clustering.EditDistance
P. Pantel, D. Lin
Document clustering with committees
In: Proc. 25th ACM SIGIR Conf. on Research and Development in Information Retrieval
DOI:10.1145/564376.564412
DBLP:conf/sigir/PantelL02

de.lmu.ifi.dbs.elki.evaluation.clustering.Entropy
M. Meilă
Comparing clusterings by the variation of information
In: Learning theory and kernel machines
DOI:10.1007/978-3-540-45167-9_14
DBLP:conf/colt/Meila03

de.lmu.ifi.dbs.elki.evaluation.clustering.Entropy
X. V. Nguyen, J. Epps, J. Bailey
Information theoretic measures for clusterings comparison: is a correction for chance necessary?
In: Proc. 26th Ann. Int. Conf. on Machine Learning (ICML ‘09)
DOI:10.1145/1553374.1553511
DBLP:conf/icml/NguyenEB09

de.lmu.ifi.dbs.elki.evaluation.clustering.PairCounting
B. Mirkin
Mathematical Classification and Clustering
In: Nonconvex Optimization and Its Applications
DOI:10.1007/978-1-4613-0457-9

de.lmu.ifi.dbs.elki.evaluation.clustering.SetMatchingPurity
E. Amigó, J. Gonzalo, J. Artiles, F. Verdejo
A comparison of extrinsic clustering evaluation metrics based on formal constraints
In: Information Retrieval 12(5)
DOI:10.1007/s10791-009-9106-z
DBLP:journals/ir/AmigoGAV09a

de.lmu.ifi.dbs.elki.evaluation.clustering.SetMatchingPurity
M. Meilă
Comparing clusterings
In: University of Washington, Seattle, Technical Report 418
Online: http://www.stat.washington.edu/mmp/Papers/compare-colt.pdf

de.lmu.ifi.dbs.elki.evaluation.clustering.SetMatchingPurity
Y. Zhao, G. Karypis
Criterion functions for document clustering: Experiments and analysis
In: University of Minnesota, Dep. Computer Science, Technical Report 01-40
Online: http://www-users.cs.umn.edu/~karypis/publications/Papers/PDF/vscluster.pdf

de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateCIndex
L. J. Hubert, J. R. Levin
A general statistical framework for assessing categorical clustering in free recall
In: Psychological Bulletin, Vol. 83(6)
DOI:10.1037/0033-2909.83.6.1072

de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateConcordantPairs
F. B. Baker, L. J. Hubert
Measuring the Power of Hierarchical Cluster Analysis
In: Journal of the American Statistical Association, 70(349)
DOI:10.1080/01621459.1975.10480256

de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateConcordantPairs
F. J. Rohlf
Methods of comparing classifications
In: Annual Review of Ecology and Systematics
DOI:10.1146/annurev.es.05.110174.000533

de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateDBCV
Davoud Moulavi, Pablo A. Jaskowiak, Ricardo J. G. B. Campello, Arthur Zimek, Jörg Sander
Density-Based Clustering Validation
In: Proc. 14th SIAM International Conference on Data Mining (SDM)
DOI:10.1137/1.9781611973440.96
DBLP:conf/sdm/MoulaviJCZS14

de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateDaviesBouldin
D. L. Davies, D. W. Bouldin
A Cluster Separation Measure
In: IEEE Transactions Pattern Analysis and Machine Intelligence 1(2)
DOI:10.1109/TPAMI.1979.4766909
DBLP:journals/pami/DaviesB79

de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluatePBMIndex
M. K. Pakhira, S. Bandyopadhyay, U. Maulik
Validity index for crisp and fuzzy clusters
In: Pattern recognition, 37(3)
DOI:10.1016/j.patcog.2003.06.005
DBLP:journals/pr/PakhiraBM04

de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateVarianceRatioCriteria
R. B. Calinski, J. Harabasz
A dendrite method for cluster analysis
In: Communications in Statistics - Theory and Methods 3(1)
DOI:10.1080/03610927408827101

de.lmu.ifi.dbs.elki.evaluation.clustering.pairsegments.ClusterPairSegmentAnalysis,
de.lmu.ifi.dbs.elki.evaluation.clustering.pairsegments.Segments,
de.lmu.ifi.dbs.elki.visualization.visualizers.pairsegments.CircleSegmentsVisualizer
Elke Achtert, Sascha Goldhofer, Hans-Peter Kriegel, Erich Schubert, Arthur Zimek
Evaluation of Clusterings - Metrics and Visual Support
In: Proc. 28th International Conference on Data Engineering (ICDE 2012)
DOI:10.1109/ICDE.2012.128
DBLP:conf/icde/AchtertGKSZ12

de.lmu.ifi.dbs.elki.evaluation.outlier.OutlierSmROCCurve
W. Klement, P. A. Flach, N. Japkowicz, S. Matwin
Smooth Receiver Operating Characteristics (smROC) Curves
In: European Conf. Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD’11)
DOI:10.1007/978-3-642-23783-6_13
DBLP:conf/pkdd/KlementFJM11

de.lmu.ifi.dbs.elki.evaluation.scores.DCGEvaluation,
de.lmu.ifi.dbs.elki.evaluation.scores.NDCGEvaluation
K. Järvelin, J. Kekäläinen
Cumulated gain-based evaluation of IR techniques
In: ACM Transactions on Information Systems (TOIS)
DOI:10.1145/582415.582418
DBLP:journals/tois/JarvelinK02

de.lmu.ifi.dbs.elki.index.idistance.InMemoryIDistanceIndex
C. Yu, B. C. Ooi, K. L. Tan, H. V. Jagadish
Indexing the distance: An efficient method to knn processing
In: Proc. 27th Int. Conf. on Very Large Data Bases
Online: http://www.vldb.org/conf/2001/P421.pdf
DBLP:conf/vldb/OoiYTJ01

de.lmu.ifi.dbs.elki.index.idistance.InMemoryIDistanceIndex
H. V. Jagadish, B. C. Ooi, K. L. Tan, C. Yu, R. Zhang
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search
In: ACM Transactions on Database Systems (TODS), 30(2)
DOI:10.1145/1071610.1071612
DBLP:journals/tods/JagadishOTYZ05

de.lmu.ifi.dbs.elki.index.lsh.hashfamilies.CosineHashFunctionFamily,
de.lmu.ifi.dbs.elki.index.lsh.hashfunctions.CosineLocalitySensitiveHashFunction
M. S. Charikar
Similarity estimation techniques from rounding algorithms
In: Proc. 34th ACM Symposium on Theory of Computing, STOC’02
DOI:10.1145/509907.509965
DBLP:conf/stoc/Charikar02

de.lmu.ifi.dbs.elki.index.preprocessed.knn.NNDescent
W. Dong, C. Moses, K. Li
Efficient k-nearest neighbor graph construction for generic similarity measures
In: Proc. 20th Int. Conf. on World Wide Web (WWW’11)
DOI:10.1145/1963405.1963487
DBLP:conf/www/DongCL11

de.lmu.ifi.dbs.elki.index.preprocessed.knn.NaiveProjectedKNNPreprocessor,
de.lmu.ifi.dbs.elki.index.preprocessed.knn.SpacefillingKNNPreprocessor,
de.lmu.ifi.dbs.elki.index.preprocessed.knn.SpacefillingMaterializeKNNPreprocessor
Erich Schubert, Arthur Zimek, Hans-Peter Kriegel
Fast and Scalable Outlier Detection with Approximate Nearest Neighbor Ensembles
In: Proc. 20th Int. Conf. Database Systems for Advanced Applications (DASFAA 2015)
DOI:10.1007/978-3-319-18123-3_2
DBLP:conf/dasfaa/SchubertZK15

de.lmu.ifi.dbs.elki.index.preprocessed.knn.RandomSampleKNNPreprocessor
Arthur Zimek, Matthew Gaudet, Ricardo J. G. B. Campello, Jörg Sander
Subsampling for Efficient and Effective Unsupervised Outlier Detection Ensembles
In: Proc. 19th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, KDD ‘13
DOI:10.1145/2487575.2487676
DBLP:conf/kdd/ZimekGCS13

de.lmu.ifi.dbs.elki.index.projected.PINN
T. de Vries, S. Chawla, M. E. Houle
Finding local anomalies in very high dimensional space
In: Proc. IEEE 10th International Conference on Data Mining (ICDM)
DOI:10.1109/ICDM.2010.151
DBLP:conf/icdm/VriesCH10

de.lmu.ifi.dbs.elki.index.tree.metrical.covertree.CoverTree
A. Beygelzimer, S. Kakade, J. Langford
Cover trees for nearest neighbor
In: In Proc. 23rd Int. Conf. Machine Learning (ICML 2006)
DOI:10.1145/1143844.1143857
DBLP:conf/icml/BeygelzimerKL06

de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree.MTree,
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.insert.MinimumEnlargementInsert,
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.MLBDistSplit,
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.MMRadSplit,
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.MRadSplit,
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.RandomSplit,
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.distribution.BalancedDistribution,
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.distribution.GeneralizedHyperplaneDistribution
P. Ciaccia, M. Patella, P. Zezula
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
In: Proc. Int. Conf. Very Large Data Bases (VLDB’97)
Online: http://www.vldb.org/conf/1997/P426.PDF
DBLP:conf/vldb/CiacciaPZ97

de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.MSTSplit
C. Traina Jr., A. J. M. Traina, B. Seeger, C. Faloutsos
Slim-Trees: High Performance Metric Trees Minimizing Overlap Between Nodes
In: Int. Conf. Extending Database Technology (EDBT’2000)
DOI:10.1007/3-540-46439-5_4
DBLP:conf/edbt/TrainaTSF00

de.lmu.ifi.dbs.elki.index.tree.spatial.kd.MinimalisticMemoryKDTree,
de.lmu.ifi.dbs.elki.index.tree.spatial.kd.SmallMemoryKDTree,
de.lmu.ifi.dbs.elki.math.spacefillingcurves.BinarySplitSpatialSorter
J. L. Bentley
Multidimensional binary search trees used for associative searching
In: Communications of the ACM 18(9)
DOI:10.1145/361002.361007
DBLP:journals/cacm/Bentley75

de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query.EuclideanRStarTreeKNNQuery,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query.RStarTreeKNNQuery
G. R. Hjaltason, H. Samet
Ranking in spatial databases
In: 4th Symp. Advances in Spatial Databases (SSD’95)
DOI:10.1007/3-540-60159-7_6
DBLP:conf/ssd/HjaltasonS95

de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query.EuclideanRStarTreeRangeQuery,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query.RStarTreeRangeQuery
J. Kuan, P. Lewis
Fast k nearest neighbour search for R-tree family
In: Proc. Int. Conf Information, Communications and Signal Processing, ICICS 1997
DOI:10.1109/ICICS.1997.652114

de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar.RStarTree,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.insert.ApproximativeLeastOverlapInsertionStrategy,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.insert.CombinedInsertionStrategy,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.insert.LeastEnlargementWithAreaInsertionStrategy,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.insert.LeastOverlapInsertionStrategy,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.overflow.LimitedReinsertOverflowTreatment,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.reinsert.CloseReinsert,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.reinsert.FarReinsert,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.split.TopologicalSplitter
Norbert Beckmann, Hans-Peter Kriegel, Ralf Schneider, Bernhard Seeger
The R*-tree: an efficient and robust access method for points and rectangles
In: Proc. 1990 ACM SIGMOD Int. Conf. Management of Data
DOI:10.1145/93597.98741
DBLP:conf/sigmod/BeckmannKSS90

de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.bulk.OneDimSortBulkSplit
N. Roussopoulos, D. Leifker
Direct spatial search on pictorial databases using packed R-trees
In: ACM SIGMOD Record 14-4
DOI:10.1145/971699.318900

de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.bulk.SortTileRecursiveBulkSplit
S. T. Leutenegger, M. A. Lopez, J. Edgington
STR: A simple and efficient algorithm for R-tree packing
In: Proc. 13th International Conference on Data Engineering (ICDE 1997)
DOI:10.1109/ICDE.1997.582015
DBLP:conf/icde/LeuteneggerEL97

de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.bulk.SpatialSortBulkSplit
I. Kamel, C. Faloutsos
On packing R-trees
In: Proc. 2nd Int. Conf. on Information and Knowledge Management
DOI:10.1145/170088.170403
DBLP:conf/cikm/KamelF93

de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.insert.LeastEnlargementInsertionStrategy,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.split.RTreeLinearSplit,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.split.RTreeQuadraticSplit
A. Guttman
R-Trees: A Dynamic Index Structure For Spatial Searching
In: Proc. 1984 ACM SIGMOD Int. Conf. on Management of Data
DOI:10.1145/971697.602266

de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.split.AngTanLinearSplit
C. H. Ang, T. C. Tan
New linear node splitting algorithm for R-trees
In: Proc. 5th Int. Sym. on Advances in Spatial Databases
DOI:10.1007/3-540-63238-7_38
DBLP:conf/ssd/AngT97

de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.split.GreeneSplit
D, Greene
An implementation and performance analysis of spatial data access methods
In: Proceedings of the Fifth International Conference on Data Engineering
DOI:10.1109/ICDE.1989.47268
DBLP:conf/icde/Greene89

de.lmu.ifi.dbs.elki.index.vafile.DAFile,
de.lmu.ifi.dbs.elki.index.vafile.PartialVAFile
Hans-Peter Kriegel, Peer Kröger, Matthias Schubert, Ziyue Zhu
Efficient Query Processing in Arbitrary Subspaces Using Vector Approximations
In: Proc. 18th Int. Conf. on Scientific and Statistical Database Management (SSDBM 06)
DOI:10.1109/SSDBM.2006.23
DBLP:conf/ssdbm/KriegelKSZ06

de.lmu.ifi.dbs.elki.index.vafile.VAFile
R. Weber, S. Blott
An approximation based data structure for similarity search
In: Report TR1997b, ETH Zentrum, Zurich, Switzerland
Online: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.40.480&rep=rep1&type=pdf

de.lmu.ifi.dbs.elki.math.Mean
P. M. Neely
Comparison of Several Algorithms for Computation of Means, Standard Deviations and Correlation Coefficients
In: Communications of the ACM 9(7), 1966
DOI:10.1145/365719.365958

de.lmu.ifi.dbs.elki.math.MeanVariance
B. P. Welford
Note on a method for calculating corrected sums of squares and products
In: Technometrics 4(3)
DOI:10.2307/1266577

de.lmu.ifi.dbs.elki.math.MeanVariance,
de.lmu.ifi.dbs.elki.math.StatisticalMoments
Erich Schubert, Michael Gertz
Numerically Stable Parallel Computation of (Co-)Variance
In: Proc. 30th Int. Conf. Scientific and Statistical Database Management (SSDBM 2018)
DOI:10.1145/3221269.3223036
DBLP:conf/ssdbm/SchubertG18

de.lmu.ifi.dbs.elki.math.MeanVariance,
de.lmu.ifi.dbs.elki.math.StatisticalMoments
E. A. Youngs, E. M. Cramer
Some Results Relevant to Choice of Sum and Sum-of-Product Algorithms
In: Technometrics 13(3)
DOI:10.1080/00401706.1971.10488826

de.lmu.ifi.dbs.elki.math.MeanVariance
D. H. D. West
Updating Mean and Variance Estimates: An Improved Method
In: Communications of the ACM 22(9)
DOI:10.1145/359146.359153
DBLP:journals/cacm/West79

de.lmu.ifi.dbs.elki.math.StatisticalMoments
T. B. Terriberry
Computing Higher-Order Moments Online
Online: http://people.xiph.org/~tterribe/notes/homs.html

de.lmu.ifi.dbs.elki.math.StatisticalMoments
P. Pébay
Formulas for Robust, One-Pass Parallel Computation of Covariances and Arbitrary-Order Statistical Moments
In: Sandia Report SAND2008-6212, Sandia National Laboratories
Online: https://prod.sandia.gov/techlib-noauth/access-control.cgi/2008/086212.pdf

de.lmu.ifi.dbs.elki.math.geodesy.SphereUtil
E. Williams
Aviation Formulary
Online: http://www.edwilliams.org/avform.htm

de.lmu.ifi.dbs.elki.math.geodesy.SphereUtil
T. Vincenty
Direct and inverse solutions of geodesics on the ellipsoid with application of nested equations
In: Survey Review 23:176
DOI:10.1179/sre.1975.23.176.88

de.lmu.ifi.dbs.elki.math.geodesy.SphereUtil
R. W. Sinnott
Virtues of the Haversine
In: Sky and Telescope 68(2)

de.lmu.ifi.dbs.elki.math.geometry.GrahamScanConvexHull2D
P. Graham
An Efficient Algorithm for Determining the Convex Hull of a Finite Planar Set
In: Information Processing Letters 1
DOI:10.1016/0020-0190(72)90045-2
DBLP:journals/ipl/Graham72

de.lmu.ifi.dbs.elki.math.geometry.PrimsMinimumSpanningTree
R. C. Prim
Shortest connection networks and some generalizations
In: Bell System Technical Journal, 36 (1957)
DOI:10.1002/j.1538-7305.1957.tb01515.x

de.lmu.ifi.dbs.elki.math.geometry.SweepHullDelaunay2D
D. Sinclair
S-hull: a fast sweep-hull routine for Delaunay triangulation
Online: http://s-hull.org/

de.lmu.ifi.dbs.elki.math.linearalgebra.pca.AutotuningPCA,
de.lmu.ifi.dbs.elki.math.linearalgebra.pca.WeightedCovarianceMatrixBuilder
Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek
A General Framework for Increasing the Robustness of PCA-based Correlation Clustering Algorithms
In: Proc. 20th Intl. Conf. on Scientific and Statistical Database Management (SSDBM)
DOI:10.1007/978-3-540-69497-7_27
DBLP:conf/ssdbm/KriegelKSZ08

de.lmu.ifi.dbs.elki.math.linearalgebra.pca.RANSACCovarianceMatrixBuilder
M. A. Fischler, R. C. Bolles
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
In: Communications of the ACM 24(6)
DOI:10.1145/358669.358692
DBLP:journals/cacm/FischlerB81

de.lmu.ifi.dbs.elki.math.spacefillingcurves.HilbertSpatialSorter
D. Hilbert
Ueber die stetige Abbildung einer Linie auf ein Flächenstück
In: Mathematische Annalen, 38(3)
Online: http://resolver.sub.uni-goettingen.de/purl?GDZPPN002253135

de.lmu.ifi.dbs.elki.math.spacefillingcurves.PeanoSpatialSorter
G. Peano
Sur une courbe, qui remplit toute une aire plane
In: Mathematische Annalen 36(1)
Online: http://resolver.sub.uni-goettingen.de/purl?GDZPPN002252376

de.lmu.ifi.dbs.elki.math.statistics.ProbabilityWeightedMoments,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.GeneralizedExtremeValueLMMEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.GeneralizedParetoLMMEstimator
J. R. M. Hosking, J. R. Wallis, E. F. Wood
Estimation of the generalized extreme-value distribution by the method of probability-weighted moments.
In: Technometrics 27.3
DOI:10.1080/00401706.1985.10488049

de.lmu.ifi.dbs.elki.math.statistics.ProbabilityWeightedMoments,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.ExponentialLMMEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.GammaLMMEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.GeneralizedLogisticAlternateLMMEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.GumbelLMMEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LogNormalLMMEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LogisticLMMEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.NormalLMMEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.SkewGNormalLMMEstimator,
de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.LMomentsEstimator
J. R. M. Hosking
Fortran routines for use with the method of L-moments Version 3.03
In: IBM Research Technical Report

de.lmu.ifi.dbs.elki.math.statistics.dependence.DistanceCorrelationDependenceMeasure
G. J. Székely, M. L. Rizzo, N. K. Bakirov
Measuring and testing dependence by correlation of distances
In: The Annals of Statistics, 35(6), 2769-2794
DOI:10.1214/009053607000000505

de.lmu.ifi.dbs.elki.math.statistics.dependence.HSMDependenceMeasure
A. Tatu, G. Albuquerque, M. Eisemann, P. Bak, H. Theisel, M. A. Magnor, D. A. Keim
Automated Analytical Methods to Support Visual Exploration of High-Dimensional Data
In: IEEE Trans. Visualization and Computer Graphics
DOI:10.1109/TVCG.2010.242
DBLP:journals/tvcg/TatuAEBTMK11

de.lmu.ifi.dbs.elki.math.statistics.dependence.HiCSDependenceMeasure,
de.lmu.ifi.dbs.elki.math.statistics.dependence.SURFINGDependenceMeasure,
de.lmu.ifi.dbs.elki.math.statistics.dependence.SlopeDependenceMeasure,
de.lmu.ifi.dbs.elki.math.statistics.dependence.SlopeInversionDependenceMeasure,
de.lmu.ifi.dbs.elki.visualization.parallel3d.OpenGL3DParallelCoordinates,
de.lmu.ifi.dbs.elki.visualization.parallel3d.Parallel3DRenderer,
de.lmu.ifi.dbs.elki.visualization.parallel3d.layout.CompactCircularMSTLayout3DPC,
de.lmu.ifi.dbs.elki.visualization.parallel3d.layout.MultidimensionalScalingMSTLayout3DPC,
de.lmu.ifi.dbs.elki.visualization.parallel3d.layout.SimpleCircularMSTLayout3DPC
Elke Achtert, Hans-Peter Kriegel, Erich Schubert, Arthur Zimek
Interactive Data Mining with 3D-Parallel-Coordinate-Trees
In: Proc. 2013 ACM Int. Conf. on Management of Data (SIGMOD 2013)
DOI:10.1145/2463676.2463696
DBLP:conf/sigmod/AchtertKSZ13

de.lmu.ifi.dbs.elki.math.statistics.dependence.HoeffdingsDDependenceMeasure
W. Hoeffding
A non-parametric test of independence
In: The Annals of Mathematical Statistics 19
Online: http://www.jstor.org/stable/2236021

de.lmu.ifi.dbs.elki.math.statistics.dependence.MCEDependenceMeasure
D. Guo
Coordinating computational and visual approaches for interactive feature selection and multivariate clustering
In: Information Visualization, 2(4)
DOI:10.1057/palgrave.ivs.9500053
DBLP:journals/ivs/Guo03

de.lmu.ifi.dbs.elki.math.statistics.dependence.SURFINGDependenceMeasure
Christian Baumgartner, Claudia Plant, Karin Kailing, Hans-Peter Kriegel, Peer Kröger
Subspace Selection for Clustering High-Dimensional Data
In: Proc. IEEE International Conference on Data Mining (ICDM 2004)
DOI:10.1109/ICDM.2004.10112
DBLP:conf/icdm/BaumgartnerPKKK04

de.lmu.ifi.dbs.elki.math.statistics.distribution.ChiSquaredDistribution,
de.lmu.ifi.dbs.elki.math.statistics.distribution.GammaDistribution
D. J. Best, D. E. Roberts
Algorithm AS 91: The percentage points of the χ² distribution
In: Journal of the Royal Statistical Society. Series C (Applied Statistics)
DOI:10.2307/2347113

de.lmu.ifi.dbs.elki.math.statistics.distribution.GammaDistribution
J. M. Bernando
Algorithm AS 103: Psi (Digamma) Function
In: Statistical Algorithms
DOI:10.2307/2347257

de.lmu.ifi.dbs.elki.math.statistics.distribution.GammaDistribution
J. H. Ahrens, U. Dieter
Computer methods for sampling from gamma, beta, Poisson and binomial distributions
In: Computing 12
DOI:10.1007/BF02293108
DBLP:journals/computing/AhrensD74

de.lmu.ifi.dbs.elki.math.statistics.distribution.GammaDistribution
J. H. Ahrens, U. Dieter
Generating gamma variates by a modified rejection technique
In: Communications of the ACM 25
DOI:10.1145/358315.358390
DBLP:journals/cacm/AhrensD82

de.lmu.ifi.dbs.elki.math.statistics.distribution.HaltonUniformDistribution
X. Wang, F. J. Hickernell
Randomized halton sequences
In: Mathematical and Computer Modelling Vol. 32 (7)
DOI:10.1016/S0895-7177(00)00178-3

de.lmu.ifi.dbs.elki.math.statistics.distribution.NormalDistribution
G. Marsaglia
Evaluating the Normal Distribution
In: Journal of Statistical Software 11(4)
DOI:10.18637/jss.v011.i04

de.lmu.ifi.dbs.elki.math.statistics.distribution.NormalDistribution
T. Ooura
Gamma / Error Functions
Online: http://www.kurims.kyoto-u.ac.jp/~ooura/gamerf.html

de.lmu.ifi.dbs.elki.math.statistics.distribution.PoissonDistribution
C. Loader
Fast and accurate computation of binomial probabilities
Online: http://projects.scipy.org/scipy/raw-attachment/ticket/620/loader2000Fast.pdf

de.lmu.ifi.dbs.elki.math.statistics.distribution.SkewGeneralizedNormalDistribution
J. R. M. Hosking, J. R. Wallis
Regional frequency analysis: an approach based on L-moments
In: Regional frequency analysis: an approach based on L-moments
DOI:10.1017/CBO9780511529443

de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.CauchyMADEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.ExponentialMADEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.GumbelMADEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LaplaceMADEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LogLogisticMADEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.RayleighMADEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.UniformMADEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.WeibullLogMADEstimator
D. J. Olive
Applied Robust Statistics
Online: http://lagrange.math.siu.edu/Olive/preprints.htm

de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.EMGOlivierNorbergEstimator
J. Olivier, M. M. Norberg
Positively skewed data: Revisiting the Box-Cox power transformation
In: International Journal of Psychological Research 3(1)
DOI:10.21500/20112084.846

de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.ExponentialMedianEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LogisticMADEstimator
D. J. Olive
Robust Estimators for Transformed Location Scale Families

de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.GammaChoiWetteEstimator
S. C. Choi, R. Wette
Maximum likelihood estimation of the parameters of the gamma distribution and their bias
In: Technometrics
DOI:10.2307/1266892

de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.GammaMOMEstimator
G. Casella, R. L. Berger
Point Estimation (Chapter 7)
In: Statistical inference. Vol. 70

de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LaplaceMLEEstimator
R. M. Norton
The Double Exponential Distribution: Using Calculus to Find a Maximum Likelihood Estimator
In: The American Statistician 38 (2)
DOI:10.2307/2683252

de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LogNormalBilkovaLMMEstimator
D. Bílková
Lognormal distribution and using L-moment method for estimating its parameters
In: Int. Journal of Mathematical Models and Methods in Applied Sciences (NAUN)
Online: http://www.naun.org/multimedia/NAUN/m3as/17-079.pdf

de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LogNormalLogMADEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.NormalMADEstimator
F. R. Hampel
The Influence Curve and Its Role in Robust Estimation
In: Journal of the American Statistical Association, June 1974, Vol. 69, No. 346
DOI:10.2307/2285666

de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.meta.WinsorizingEstimator
C. Hastings, F. Mosteller, J. W. Tukey, C. P. Winsor
Low moments for small samples: a comparative study of order statistics
In: The Annals of Mathematical Statistics, 18(3)
DOI:10.1214/aoms/1177730388

de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.ALIDEstimator
Oussama Chelly, Michael E. Houle, Ken-ichi Kawarabayashi
Enhanced Estimation of Local Intrinsic Dimensionality Using Auxiliary Distances
In: Contributed to ELKI

de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.AggregatedHillEstimator
R. Huisman, K. G. Koedijk, C. J. M. Kool, F. Palm
Tail-Index Estimates in Small Samples
In: Journal of Business & Economic Statistics
DOI:10.1198/073500101316970421

de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.GEDEstimator
M. E. Houle, H. Kashima, M. Nett
Generalized expansion dimension
In: 12th International Conference on Data Mining Workshops (ICDMW)
DOI:10.1109/ICDMW.2012.94
DBLP:conf/icdm/HouleKN12

de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.HillEstimator
B. M. Hill
A simple general approach to inference about the tail of a distribution
In: The annals of statistics 3(5)
DOI:10.1214/aos/1176343247

de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.LMomentsEstimator,
de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.MOMEstimator,
de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.PWM2Estimator,
de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.PWMEstimator,
de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.RVEstimator
L. Amsaleg, O. Chelly, T. Furon, S. Girard, M. E. Houle, K. Kawarabayashi, M. Nett
Estimating Local Intrinsic Dimensionality
In: Proc. SIGKDD International Conference on Knowledge Discovery and Data Mining 2015
DOI:10.1145/2783258.2783405
DBLP:conf/kdd/AmsalegCFGHKN15

de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.PWM2Estimator,
de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.PWMEstimator
J. Maciunas Landwehr, N. C. Matalas, J. R. Wallis
Probability weighted moments compared with some traditional techniques in estimating Gumbel parameters and quantiles
In: Water Resources Research 15(5)
DOI:10.1029/WR015i005p01055

de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.ZipfEstimator
J. Beirlant, G. Dierckx, A. Guillou
Estimation of the extreme-value index and generalized quantile plots
In: Bernoulli 11(6)
DOI:10.3150/bj/1137421635

de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.ZipfEstimator
J. Schultze, J. Steinebach
On Least Squares Estimates of an Exponential Tail Coefficient
In: Statistics & Risk Modeling 14(4)
DOI:10.1524/strm.1996.14.4.353

de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.ZipfEstimator
M. Kratz, S. I. Resnick
The QQ-estimator and heavy tails
In: Communications in Statistics. Stochastic Models 12(4)
DOI:10.1080/15326349608807407

de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.BiweightKernelDensityFunction,
de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.EpanechnikovKernelDensityFunction,
de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.GaussianKernelDensityFunction,
de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.KernelDensityFunction,
de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.TriweightKernelDensityFunction,
de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.UniformKernelDensityFunction
J. S. Marron, D. Nolan
Canonical kernels for density estimation
In: Statistics & Probability Letters, Volume 7, Issue 3
DOI:10.1016/0167-7152(88)90050-8

de.lmu.ifi.dbs.elki.math.statistics.tests.AndersonDarlingTest
T. W. Anderson, D. A. Darling
Asymptotic theory of certain ‘goodness of fit’ criteria based on stochastic processes
In: Annals of mathematical statistics 23(2)
DOI:10.1214/aoms/1177729437

de.lmu.ifi.dbs.elki.math.statistics.tests.AndersonDarlingTest
M. A. Stephens
EDF Statistics for Goodness of Fit and Some Comparisons
In: Journal of the American Statistical Association, Volume 69, Issue 347
DOI:10.1080/01621459.1974.10480196

de.lmu.ifi.dbs.elki.math.statistics.tests.StandardizedTwoSampleAndersonDarlingTest
A. N. Pettitt
A two-sample Anderson-Darling rank statistic
In: Biometrika 63 (1)
DOI:10.1093/biomet/63.1.161

de.lmu.ifi.dbs.elki.math.statistics.tests.StandardizedTwoSampleAndersonDarlingTest
F. W. Scholz, M. A. Stephens
K-sample Anderson–Darling tests
In: Journal of the American Statistical Association, 82(399)
DOI:10.1080/01621459.1987.10478517

de.lmu.ifi.dbs.elki.math.statistics.tests.StandardizedTwoSampleAndersonDarlingTest
D. A. Darling
The Kolmogorov-Smirnov, Cramer-von Mises tests
In: Annals of mathematical statistics 28(4)
DOI:10.1214/aoms/1177706788

de.lmu.ifi.dbs.elki.result.KMLOutputHandler
Erich Achtert, Ahmed Hettab, Hans-Peter Kriegel, Erich Schubert, Arthur Zimek
Spatial Outlier Detection: Data, Algorithms, Visualizations
In: Proc. 12th Int. Symp. Spatial and Temporal Databases (SSTD 2011)
DOI:10.1007/978-3-642-22922-0_41
DBLP:conf/ssd/AchtertHKSZ11

de.lmu.ifi.dbs.elki.utilities.datastructures.unionfind.WeightedQuickUnionInteger,
de.lmu.ifi.dbs.elki.utilities.datastructures.unionfind.WeightedQuickUnionRangeDBIDs,
de.lmu.ifi.dbs.elki.utilities.datastructures.unionfind.WeightedQuickUnionStaticDBIDs
R. Sedgewick
Algorithms in C, Parts 1-4
DBLP:books/daglib/0004943

de.lmu.ifi.dbs.elki.utilities.random.FastNonThreadsafeRandom,
de.lmu.ifi.dbs.elki.utilities.random.XorShift1024NonThreadsafeRandom,
de.lmu.ifi.dbs.elki.utilities.random.XorShift64NonThreadsafeRandom,
de.lmu.ifi.dbs.elki.utilities.random.Xoroshiro128NonThreadsafeRandom
D. Lemire
Fast random shuffling
In: Daniel Lemire’s blog
Online: http://lemire.me/blog/2016/06/30/fast-random-shuffling/

de.lmu.ifi.dbs.elki.utilities.random.XorShift1024NonThreadsafeRandom,
de.lmu.ifi.dbs.elki.utilities.random.XorShift64NonThreadsafeRandom
S. Vigna
An experimental exploration of Marsaglia’s xorshift generators, scrambled
Online: http://vigna.di.unimi.it/ftp/papers/xorshift.pdf

de.lmu.ifi.dbs.elki.utilities.random.Xoroshiro128NonThreadsafeRandom
D. Blackman, S. Vigna
xoroshiro+ / xorshift* / xorshift+ generators and the PRNG shootout
Online: http://xoroshiro.di.unimi.it/

de.lmu.ifi.dbs.elki.utilities.scaling.outlier.COPOutlierScaling,
de.lmu.ifi.dbs.elki.utilities.scaling.outlier.MinusLogGammaScaling,
de.lmu.ifi.dbs.elki.utilities.scaling.outlier.MinusLogStandardDeviationScaling,
de.lmu.ifi.dbs.elki.utilities.scaling.outlier.MultiplicativeInverseScaling,
de.lmu.ifi.dbs.elki.utilities.scaling.outlier.OutlierGammaScaling,
de.lmu.ifi.dbs.elki.utilities.scaling.outlier.OutlierMinusLogScaling,
de.lmu.ifi.dbs.elki.utilities.scaling.outlier.SqrtStandardDeviationScaling,
de.lmu.ifi.dbs.elki.utilities.scaling.outlier.StandardDeviationScaling
Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek
Interpreting and Unifying Outlier Scores
In: Proc. 11th SIAM International Conference on Data Mining (SDM 2011)
DOI:10.1137/1.9781611972818.2
DBLP:conf/sdm/KriegelKSZ11

de.lmu.ifi.dbs.elki.utilities.scaling.outlier.HeDESNormalizationOutlierScaling
H. V. Nguyen, H. H. Ang, V. Gopalkrishnan
Mining Outliers with Ensemble of Heterogeneous Detectors on Random Subspaces
In: Proc. 15th Int. Conf. Database Systems for Advanced Applications (DASFAA 2010)
DOI:10.1007/978-3-642-12026-8_29
DBLP:conf/dasfaa/VuAG10

de.lmu.ifi.dbs.elki.utilities.scaling.outlier.MixtureModelOutlierScaling,
de.lmu.ifi.dbs.elki.utilities.scaling.outlier.SigmoidOutlierScaling
J. Gao, P.-N. Tan
Converting Output Scores from Outlier Detection Algorithms into Probability Estimates
In: Proc. Sixth International Conference on Data Mining, 2006. ICDM’06.
DOI:10.1109/ICDM.2006.43
DBLP:conf/icdm/GaoT06

de.lmu.ifi.dbs.elki.visualization.projector.ParallelPlotProjector
A. Inselberg
Parallel Coordinates. Visual Multidimensional Geometry and Its Applications
DOI:10.1007/978-0-387-68628-8

de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.density.DensityEstimationOverlay.Instance
D. W. Scott
Multivariate density estimation: Theory, Practice, and Visualization
In: Multivariate Density Estimation: Theory, Practice, and Visualization
DOI:10.1002/9780470316849

de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.outlier.BubbleVisualization
Elke Achtert, Hans-Peter Kriegel, Lisa Reichert, Erich Schubert, Remigius Wojdanowski, Arthur Zimek
Visual Evaluation of Outlier Detection Models
In: Proc. 15th Int. Conf. on Database Systems for Advanced Applications (DASFAA 2010)
DOI:10.1007/978-3-642-12098-5_34
DBLP:conf/dasfaa/AchtertKRSWZ10