layout	title	short	parent
page	Publications Implemented or Referenced by ELKI	Implemented Papers	algorithms

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.
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elki.math.statistics.intrinsicdimensionality.ABIDEstimator,
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elki.math.statistics.intrinsicdimensionality.LMomentsEstimator,
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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

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

elki.outlier.anglebased.ABOD,
elki.outlier.anglebased.FastABOD,
elki.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

elki.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

elki.outlier.density.HySortOD
Eugênio F. Cabral, and Robson L.F. Cordeiro
Fast and Scalable Outlier Detection with Sorted Hypercubes
In: Proc. 29th ACM Int. Conf. on Information & Knowledge Management (CIKM'20)
DOI:10.1145/3340531.3412033

elki.outlier.density.IsolationForest
F. T. Liu, K. M. Ting, Z.-H. Zhou
Isolation-Based Anomaly Detection
In: Transactions on Knowledge Discovery from Data (TKDD)
DOI:10.1145/2133360.2133363
DBLP:journals/tkdd/LiuTZ12

elki.outlier.distance.AbstractDBOutlier,
elki.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

elki.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

elki.outlier.distance.HilOut,
elki.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

elki.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

elki.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

elki.outlier.distance.KNNSOS,
elki.outlier.distance.SOS,
elki.outlier.intrinsic.ISOS,
elki.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

elki.outlier.distance.KNNSOS,
elki.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

elki.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

elki.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

elki.outlier.distance.parallel.ParallelKNNOutlier,
elki.outlier.distance.parallel.ParallelKNNWeightOutlier,
elki.outlier.lof.parallel,
elki.outlier.lof.parallel.ParallelLOF,
elki.outlier.lof.parallel.ParallelSimplifiedLOF,
elki.outlier.lof.SimplifiedLOF
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

elki.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

elki.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

elki.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

elki.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

elki.outlier.intrinsic.LID
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)
DOI:10.1007/978-3-030-02224-2_14
DBLP:conf/sisap/HouleSZ18

elki.outlier.lof.ALOCI,
elki.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

elki.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

elki.outlier.lof.FlexibleLOF,
elki.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

elki.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

elki.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

elki.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

elki.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

elki.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

elki.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

elki.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

elki.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

elki.outlier.SimpleCOP
Arthur Zimek
Application 2: Outlier Detection (Chapter 18)
In: Correlation Clustering

elki.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

elki.outlier.spatial.CTLuMeanMultipleAttributes,
elki.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

elki.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

elki.outlier.spatial.CTLuMoranScatterplotOutlier,
elki.outlier.spatial.CTLuScatterplotOutlier,
elki.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

elki.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

elki.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

elki.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

elki.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

elki.outlier.subspace.AbstractAggarwalYuOutlier,
elki.outlier.subspace.AggarwalYuEvolutionary,
elki.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

elki.outlier.subspace.OutRankS1
E. Müller, I. Assent, U. Steinhausen, T. 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

elki.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

elki.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

elki.outlier.svm.LibSVMOneClassOutlierDetection,
elki.outlier.svm.OCSVM
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

elki.outlier.svm.SVDD
D. M. J. Tax and R. P. W. Duin
Support Vector Data Description
In: Mach. Learn. 54(1): 45-66
DOI:10.1023/B:MACH.0000008084.60811.49
DBLP:journals/ml/TaxD04

elki.projection.BarnesHutTSNE,
elki.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

elki.projection.GaussianAffinityMatrixBuilder,
elki.projection.PerplexityAffinityMatrixBuilder,
elki.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

elki.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

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

elki.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

elki.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

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

elki.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

elki.utilities.datastructures.KuhnMunkres
J. Munkres
Algorithms for the Assignment and Transportation Problems
In: Journal of the Society for Industrial and Applied Mathematics 5(1)
DOI:10.1137/0105003

elki.utilities.datastructures.KuhnMunkres
H. W. Kuhn
The Hungarian method for the assignment problem
In: Naval Research Logistics Quarterly 2
DOI:10.1002/nav.3800020109

elki.utilities.datastructures.KuhnMunkresStern
K. L. Stern
The Hungarian Algorithm for the Assignment Problem
Online: http://software-and-algorithms.blogspot.com/2012/09/the-hungarian-algorithm-for-assignment.html

elki.utilities.datastructures.KuhnMunkresWong
J. K. Wong
A new implementation of an algorithm for the optimal assignment problem: An improved version of Munkres' algorithm
In: BIT Numerical Mathematics 19(3)
DOI:10.1007/BF01930994

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

elki.utilities.random.FastNonThreadsafeRandom
D. Lemire
Fast Random Integer Generation in an Interval
In: ACM Trans. Model. Comput. Simul. 29(1)
DOI:10.1145/3230636
DBLP:journals/tomacs/Lemire19

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

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

elki.utilities.random.XorShift1024NonThreadsafeRandom,
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

elki.utilities.scaling.outlier.COPOutlierScaling,
elki.utilities.scaling.outlier.MinusLogGammaScaling,
elki.utilities.scaling.outlier.MinusLogStandardDeviationScaling,
elki.utilities.scaling.outlier.MultiplicativeInverseScaling,
elki.utilities.scaling.outlier.OutlierGammaScaling,
elki.utilities.scaling.outlier.OutlierMinusLogScaling,
elki.utilities.scaling.outlier.SqrtStandardDeviationScaling,
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

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

elki.utilities.scaling.outlier.MixtureModelOutlierScaling,
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

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

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

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

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