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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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