Publications Implemented or Referenced by ELKI

The following publications are cited by classes in ELKI (as of ELKI 0.8.0):

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

elki.algorithm.KNNDistancesSampler,
elki.clustering.dbscan.DBSCAN,
elki.clustering.dbscan.predicates.EpsilonNeighborPredicate,
elki.clustering.dbscan.predicates.MinPtsCorePredicate,
elki.clustering.dbscan.predicates.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

elki.algorithm.KNNDistancesSampler,
elki.clustering.dbscan.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

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

elki.application.AbstractApplication
Erich Schubert
Automatic Indexing for Similarity Search in ELKI
In: Int. Conf. Similarity Search and Applications
DOI:10.1007/978-3-031-17849-8_16
DBLP:conf/sisap/Schubert22

elki.application.experiments.VisualizeGeodesicDistances,
elki.distance.geo.DimensionSelectingLatLngDistance,
elki.distance.geo.LatLngDistance,
elki.distance.geo.LngLatDistance,
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

elki.application.greedyensemble.ComputeKNNOutlierScores,
elki.application.greedyensemble.GreedyEnsembleExperiment,
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

elki.clustering.affinitypropagation.AffinityPropagation
B. J. Frey, D. Dueck
Clustering by Passing Messages Between Data Points
In: Science Vol 315
DOI:10.1126/science.1136800

elki.clustering.BetulaLeafPreClustering,
elki.clustering.em.BetulaGMM,
elki.clustering.em.BetulaGMMWeighted,
elki.clustering.em.models.BetulaClusterModel,
elki.clustering.em.models.BetulaClusterModelFactory,
elki.clustering.em.models.BetulaDiagonalGaussianModelFactory,
elki.clustering.em.models.BetulaMultivariateGaussianModelFactory,
elki.clustering.em.models.BetulaSphericalGaussianModelFactory,
elki.clustering.kmeans.BetulaLloydKMeans,
elki.clustering.kmeans.initialization.betula.CFKPlusPlusLeaves,
elki.clustering.kmeans.initialization.betula.CFKPlusPlusTree,
elki.clustering.kmeans.initialization.betula.CFKPlusPlusTrunk,
elki.clustering.kmeans.initialization.betula.CFRandomlyChosen,
elki.clustering.kmeans.initialization.betula.CFWeightedRandomlyChosen,
elki.clustering.kmeans.initialization.betula.InterclusterWeight,
elki.clustering.kmeans.initialization.betula.SquaredEuclideanWeight,
elki.clustering.kmeans.initialization.betula.VarianceWeight,
elki.index.tree.betula.CFTree,
elki.index.tree.betula.distance.AverageInterclusterDistance,
elki.index.tree.betula.distance.AverageIntraclusterDistance,
elki.index.tree.betula.distance.CentroidEuclideanDistance,
elki.index.tree.betula.distance.CentroidManhattanDistance,
elki.index.tree.betula.distance.RadiusDistance,
elki.index.tree.betula.distance.VarianceIncreaseDistance
Andreas Lang and Erich Schubert
BETULA: Fast Clustering of Large Data with Improved BIRCH CF-Trees
In: Information Systems
DOI:10.1016/j.is.2021.101918
DBLP:journals/is/LangS22

elki.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

elki.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

elki.clustering.CFSFDP
A. Rodriguez and A. Laio
Clustering by fast search and find of density peaks
In: Science 344 (6191)
DOI:10.1126/science.1242072

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

elki.clustering.correlation.COPAC,
elki.clustering.dbscan.predicates.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

elki.clustering.correlation.ERiC,
elki.clustering.dbscan.predicates.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

elki.clustering.correlation.FourC,
elki.clustering.dbscan.predicates.FourCCorePredicate,
elki.clustering.dbscan.predicates.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

elki.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

elki.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

elki.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

elki.clustering.dbscan.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

elki.clustering.dbscan.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

elki.clustering.dbscan.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

elki.clustering.dbscan.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

elki.clustering.dbscan.predicates.PreDeConCorePredicate,
elki.clustering.dbscan.predicates.PreDeConNeighborPredicate,
elki.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

elki.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

elki.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

elki.clustering.em.KDTreeEM
Andrew W. Moore
Very Fast EM-based Mixture Model Clustering using Multiresolution kd-trees
In: Advances in Neural Information Processing Systems 11 (NIPS 1998)
DBLP:conf/nips/Moore98

elki.clustering.hierarchical.AbstractHDBSCAN,
elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction,
elki.clustering.hierarchical.extraction.SimplifiedHierarchyExtraction,
elki.clustering.hierarchical.HDBSCANLinearMemory,
elki.clustering.hierarchical.SLINKHDBSCANLinearMemory
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

elki.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

elki.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

elki.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

elki.clustering.hierarchical.Anderberg,
elki.clustering.hierarchical.MiniMaxAnderberg
M. R. Anderberg
Hierarchical Clustering Methods
In: Cluster Analysis for Applications

elki.clustering.hierarchical.birch.AverageInterclusterDistance,
elki.clustering.hierarchical.birch.AverageIntraclusterDistance,
elki.clustering.hierarchical.birch.CentroidEuclideanDistance,
elki.clustering.hierarchical.birch.CentroidManhattanDistance,
elki.clustering.hierarchical.birch.VarianceIncreaseDistance,
elki.index.tree.betula.distance.BIRCHAverageInterclusterDistance,
elki.index.tree.betula.distance.BIRCHAverageIntraclusterDistance,
elki.index.tree.betula.distance.BIRCHVarianceIncreaseDistance
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

elki.clustering.hierarchical.birch.BIRCHLeafClustering,
elki.clustering.hierarchical.birch.BIRCHLloydKMeans,
elki.clustering.hierarchical.birch.CFTree,
elki.index.tree.betula.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

elki.clustering.hierarchical.birch.BIRCHLeafClustering,
elki.clustering.hierarchical.birch.BIRCHLloydKMeans,
elki.clustering.hierarchical.birch.CFTree,
elki.clustering.hierarchical.birch.DiameterCriterion,
elki.clustering.hierarchical.birch.RadiusCriterion,
elki.index.tree.betula.CFTree,
elki.index.tree.betula.distance.BIRCHRadiusDistance
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

elki.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

elki.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

elki.clustering.hierarchical.extraction.HDBSCANHierarchyExtraction,
elki.outlier.clustering.GLOSH
R. J. G. B. Campello, D. Moulavi, A. Zimek, J. Sander
Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection
In: ACM Trans. Knowl. Discov. Data 10(1)
DOI:10.1145/2733381
DBLP:journals/tkdd/CampelloMZS15

elki.clustering.hierarchical.HACAM
Erich Schubert
HACAM: Hierarchical Agglomerative Clustering Around Medoids - and its Limitations
In: Proc. Conf. “Lernen, Wissen, Daten, Analysen”, LWDA
Online: http://ceur-ws.org/Vol-2993/paper-19.pdf
DBLP:conf/lwa/Schubert21

elki.clustering.hierarchical.LinearMemoryNNChain
F. Murtagh
Multidimensional Clustering Algorithms
In: Multidimensional Clustering Algorithms
Online: http://www.multiresolutions.com/strule/MClA/

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

elki.clustering.hierarchical.linkage.CompleteLinkage,
elki.clustering.hierarchical.linkage.FlexibleBetaLinkage,
elki.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

elki.clustering.hierarchical.linkage.CompleteLinkage,
elki.distance.BrayCurtisDistance
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, Biologiske Skrifter 5(4)

elki.clustering.hierarchical.linkage.CompleteLinkage
S. C. Johnson
Hierarchical clustering schemes
In: Psychometrika 32
DOI:10.1007/BF02289588

elki.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

elki.clustering.hierarchical.linkage.GroupAverageLinkage,
elki.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

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

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

elki.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)

elki.clustering.hierarchical.linkage.WardLinkage
D. Wishart
256. Note: An Algorithm for Hierarchical Classifications
In: Biometrics 25(1)
DOI:10.2307/2528688

elki.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

elki.clustering.hierarchical.MedoidLinkage
S. Miyamoto, Y. Kaizu, Y. Endo
Hierarchical and non-hierarchical medoid clustering using asymmetric similarity measures
In: Soft Computing and Intelligent Systems (SCIS) and Int. Symp. Advanced Intelligent Systems (ISIS)
DOI:10.1109/SCIS-ISIS.2016.0091
DBLP:conf/scisisis/MiyamotoKE16

elki.clustering.hierarchical.MedoidLinkage
D. Herr, Q. Han, S. Lohmann, T. Ertl
Visual clutter reduction through hierarchy-based projection of high-dimensional labeled data
In: Graphics Interface Conference
DOI:10.20380/GI2016.14
DBLP:conf/graphicsinterface/HerrHLE16

elki.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

elki.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

elki.clustering.hierarchical.MiniMaxNNChain,
elki.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

elki.clustering.hierarchical.MiniMaxNNChain,
elki.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

elki.clustering.hierarchical.OPTICSToHierarchical
Jörg Sander, Xuejie Qin, Zhiyong Lu, Nan Niu, Alex Kovarsky
Automatic Extraction of Clusters from Hierarchical Clustering Representations
In: 7th Pacific-Asia Conf. Advances in Knowledge Discovery and Data Mining, PAKDD
DOI:10.1007/3-540-36175-8_8
DBLP:conf/pakdd/SanderQLNK03

elki.clustering.hierarchical.SLINK
R. Sibson
SLINK: An optimally efficient algorithm for the single-link cluster method
In: The Computer Journal 16 (1)
DOI:10.1093/comjnl/16.1.30
DBLP:journals/cj/Sibson73

elki.clustering.kcenter.GreedyKCenter
D. S. Hochbaum, D. B. Shmoys
A unified approach to approximation algorithms for bottleneck problems
In: Journal of the ACM, 33 (3), 1986
DOI:10.1145/5925.5933
DBLP:journals/jacm/HochbaumS86

elki.clustering.kcenter.GreedyKCenter
T. F. Gonzalez
Clustering to Minimize the Maximum Intercluster Distance
In: Theoretical Computer Science, 38
DOI:10.1016/0304-3975(85)90224-5
DBLP:journals/tcs/Gonzalez85

elki.clustering.kmeans.AnnulusKMeans
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

elki.clustering.kmeans.AnnulusKMeans
J. Drake
Faster k-means clustering
In: Faster k-means clustering
Online: http://hdl.handle.net/2104/8826

elki.clustering.kmeans.BisectingKMeans,
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

elki.clustering.kmeans.CompareMeans,
elki.clustering.kmeans.SortMeans
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

elki.clustering.kmeans.ElkanKMeans
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

elki.clustering.kmeans.ExponionKMeans,
elki.clustering.kmeans.SimplifiedElkanKMeans
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

elki.clustering.kmeans.FuzzyCMeans
J. C. Dunn
A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters
In: Journal of Cybernetics 3(3)
DOI:10.1080/01969727308546046

elki.clustering.kmeans.FuzzyCMeans
J. Bezdek
Pattern Recognition With Fuzzy Objective Function Algorithms
In: Pattern Recognition With Fuzzy Objective Function Algorithms
DOI:10.1007/978-1-4757-0450-1
DBLP:books/sp/Bezdek81

elki.clustering.kmeans.GMeans
G. Hamerly and C. Elkan
Learning the k in k-means
In: Neural Information Processing Systems
Online: https://papers.nips.cc/paper_files/paper/2003/hash/234833147b97bb6aed53a8f4f1c7a7d8-Abstract.html
DBLP:conf/nips/HamerlyE03

elki.clustering.kmeans.HamerlyKMeans
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

elki.clustering.kmeans.HartiganWongKMeans
J. A. Hartigan, M. A. Wong
Algorithm AS 136: A K-Means Clustering Algorithm
In: J. Royal Statistical Society. Series C (Applied Statistics) 28(1)
DOI:10.2307/2346830

elki.clustering.kmeans.initialization.AFKMC2
O. Bachem, M. Lucic, S. H. Hassani, A. Krause
Fast and Provably Good Seedings for k-Means
In: Neural Information Processing Systems 2016
Online: https://proceedings.neurips.cc/paper/2016/hash/d67d8ab4f4c10bf22aa353e27879133c-Abstract.html
DBLP:conf/nips/BachemLH016

elki.clustering.kmeans.initialization.FirstK,
elki.clustering.kmeans.MacQueenKMeans
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

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elki.math.statistics.distribution.ChiSquaredDistribution,
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elki.math.statistics.distribution.PoissonDistribution
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elki.math.statistics.intrinsicdimensionality.ABIDEstimator,
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elki.math.statistics.intrinsicdimensionality.LMomentsEstimator,
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elki.math.statistics.tests.StandardizedTwoSampleAndersonDarlingTest
D. A. Darling
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elki.outlier.anglebased.ABOD,
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Angle-Based Outlier Detection in High-dimensional Data
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elki.outlier.density.HySortOD
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elki.outlier.distance.KNNSOS,
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Intrinsic t-Stochastic Neighbor Embedding for Visualization and Outlier Detection: A Remedy Against the Curse of Dimensionality?
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elki.outlier.distance.KNNSOS,
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elki.outlier.distance.parallel.ParallelKNNOutlier,
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elki.outlier.lof.SimplifiedLOF
Erich Schubert, Arthur Zimek, Hans-Peter Kriegel
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elki.outlier.distance.ReferenceBasedOutlierDetection
Y. Pei, O. R. Zaiane, Y. Gao
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elki.outlier.DWOF
R. Momtaz, N. Mohssen, M. A. Gowayyed
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elki.outlier.GaussianUniformMixture
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elki.outlier.intrinsic.IDOS
Jonathan von Brünken, Michael E. Houle, Arthur Zimek
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elki.outlier.intrinsic.LID
Michael E. Houle, Erich Schubert, Arthur Zimek
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elki.outlier.lof.ALOCI,
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S. Papadimitriou, H. Kitagawa, P. B. Gibbons, C. Faloutsos
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elki.outlier.lof.COF
J. Tang, Z. Chen, A. W. C. Fu, D. W. Cheung
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elki.outlier.lof.FlexibleLOF,
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Markus M. Breunig, Hans-Peter Kriegel, Raymond Ng, Jörg Sander
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elki.outlier.lof.INFLO
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elki.outlier.lof.KDEOS
Erich Schubert, Arthur Zimek, Hans-Peter Kriegel
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elki.outlier.lof.LDF
L. J. Latecki, A. Lazarevic, D. Pokrajac
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elki.outlier.lof.LDOF
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elki.outlier.lof.LoOP
Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek
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elki.outlier.lof.VarianceOfVolume
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elki.outlier.meta.FeatureBagging
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elki.outlier.OPTICSOF
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elki.outlier.SimpleCOP
Arthur Zimek
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elki.outlier.spatial.CTLuGLSBackwardSearchAlgorithm
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elki.outlier.spatial.CTLuMeanMultipleAttributes,
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elki.outlier.spatial.CTLuMoranScatterplotOutlier,
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elki.outlier.spatial.SLOM
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elki.outlier.spatial.SOF
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elki.outlier.subspace.AbstractAggarwalYuOutlier,
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elki.outlier.subspace.SOD
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elki.outlier.svm.LibSVMOneClassOutlierDetection,
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elki.projection.BarnesHutTSNE,
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elki.projection.GaussianAffinityMatrixBuilder,
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elki.result.KMLOutputHandler
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elki.visualization.visualizers.scatterplot.outlier.BubbleVisualization
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