ELKI references overview:

de.lmu.ifi.dbs.elki.algorithm.DependencyDerivator
E. Achtert, C. Böhm, H.-P. Kriegel, P. Kröger, A. Zimek
Deriving Quantitative Dependencies for Correlation Clusters
In: Proc. 12th Int. Conf. on Knowledge Discovery and Data Mining (KDD '06), Philadelphia, PA 2006.
http://dx.doi.org/10.1145/1150402.1150408
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 international conference on Knowledge discovery and data mining
http://dx.doi.org/10.1145%2F347090.347123
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
M. Ester, H.-P. Kriegel, J. Sander, X. 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), Portland, OR, 1996
http://www.aaai.org/Papers/KDD/1996/KDD96-037
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
http://dx.doi.org/10.1109/34.400568
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), 2003
http://www.siam.org/meetings/sdm03/proceedings/sdm03_05.pdf
de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation.AffinityPropagationClusteringAlgorithm
B. J. Frey and D. Dueck
Clustering by Passing Messages Between Data Points
In: Science Vol 315
http://dx.doi.org/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 International Conference on Intelligent Systems for Molecular Biology (ISMB)
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.CASH
E. Achtert, C. Böhm, J. David, P. Kröger, A. Zimek
Robust clustering in arbitraily oriented subspaces
In: Proc. 8th SIAM Int. Conf. on Data Mining (SDM'08), Atlanta, GA, 2008
http://www.siam.org/proceedings/datamining/2008/dm08_69_AchtertBoehmDavidKroegerZimek.pdf
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.COPAC, de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.COPACNeighborPredicate
E. Achtert, C. Böhm, H.-P. Kriegel, P. Kröger, A. Zimek
Robust, Complete, and Efficient Correlation Clustering
In: Proc. 7th SIAM International Conference on Data Mining (SDM'07), Minneapolis, MN, 2007
http://www.siam.org/proceedings/datamining/2007/dm07_037achtert.pdf
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.ERiC, de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.ERiCNeighborPredicate
E. Achtert, C. Böhm, H.-P. Kriegel, P. Kröger, and A. Zimek
On Exploring Complex Relationships of Correlation Clusters
In: Proc. 19th International Conference on Scientific and Statistical Database Management (SSDBM 2007), Banff, Canada, 2007
http://dx.doi.org/10.1109/SSDBM.2007.21
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
C. Böhm, K. Kailing, P. Kröger, A. Zimek
Computing Clusters of Correlation Connected Objects
In: Proc. ACM SIGMOD Int. Conf. on Management of Data, Paris, France, 2004, 455-466
http://dx.doi.org/10.1145/1007568.1007620
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.HiCO
E. Achtert, C. Böhm, P. Kröger, A. Zimek
Mining Hierarchies of Correlation Clusters
In: Proc. Int. Conf. on Scientific and Statistical Database Management (SSDBM'06), Vienna, Austria, 2006
http://dx.doi.org/10.1109/SSDBM.2006.35
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.LMCLUS
Robert Haralick, Rave Harpaz
Linear manifold clustering in high dimensional spaces by stochastic search
In: Pattern Recognition volume 40, Issue 10
http://dx.doi.org/10.1016/j.patcog.2007.01.020
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)
http://dx.doi.org/10.1145/342009.335383
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), 1977, pp. 1-31
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
http://dx.doi.org/10.1023/A:1009745219419
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.LSDBC
E. Biçici and D. Yuret
Locally Scaled Density Based Clustering
In: Adaptive and Natural Computing Algorithms
http://dx.doi.org/10.1007/978-3-540-71618-1_82
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
C. Böhm, K. Kailing, H.-P. Kriegel, P. Kröger
Density Connected Clustering with Local Subspace Preferences
In: Proc. 4th IEEE Int. Conf. on Data Mining (ICDM'04), Brighton, UK, 2004
http://dx.doi.org/10.1109/ICDM.2004.10087
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.AGNES
L. Kaufman and P. J. Rousseeuw
Agglomerative Nesting (Program AGNES)
In: Finding Groups in Data: An Introduction to Cluster Analysis
http://dx.doi.org/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)
http://dx.doi.org/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, and J. Sander
Density-Based Clustering Based on Hierarchical Density Estimates
In: Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD
http://dx.doi.org/10.1007/978-3-642-37456-2_14
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.AnderbergHierarchicalClustering
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
http://dx.doi.org/10.1093/comjnl/20.4.364
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.CentroidLinkageMethod, de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.GroupAverageLinkageMethod, de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.WeightedAverageLinkageMethod
A. K. Jain and R. C. Dubes
Algorithms for Clustering Data
In: Algorithms for Clustering Data, Prentice-Hall
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.LinkageMethod
G. N. Lance and W. T. Williams
A general theory of classificatory sorting strategies 1. Hierarchical systems
In: The computer journal 9.4
http://dx.doi.org/ 10.1093/comjnl/9.4.373
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.MedianLinkageMethod
J. C. Gower
A comparison of some methods of cluster analysis
In: Biometrics (1967)
http://www.jstor.org/stable/10.2307/2528417
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.
http://dx.doi.org/10.1093/comjnl/16.1.30
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.SingleLinkageMethod
K. Florek and J. Łukaszewicz and J. Perkal and H. Steinhaus and S. Zubrzycki
Sur la liaison et la division des points d'un ensemble fini
In: Colloquium Mathematicae (Vol. 2, No. 3-4)
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.WardLinkageMethod
J. H. Ward Jr
Hierarchical grouping to optimize an objective function
In: Journal of the American statistical association 58.301
http://dx.doi.org/10.1080/01621459.1963.10500845
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.CLARA
L. Kaufman, P. J. Rousseeuw
Clustering Large Data Sets (with discussion)
In: Pattern Recognition in Practice II
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansBisecting
M. Steinbach, G. Karypis, V. Kumar
A Comparison of Document Clustering Techniques
In: KDD workshop on text mining. Vol. 400. No. 1
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
http://www.aaai.org/Library/ICML/2003/icml03-022.php
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
http://dx.doi.org/10.1137/1.9781611972801.12
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.
http://dx.doi.org/10.1109/TIT.1982.1056489
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansMacQueen
J. MacQueen
Some Methods for Classification and Analysis of Multivariate Observations
In: 5th Berkeley Symp. Math. Statist. Prob., Vol. 1, 1967, pp 281-297
http://projecteuclid.org/euclid.bsmsp/1200512992
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
https://papers.nips.cc/paper/1260-clustering-via-concave-minimization.pdf
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPAM
Kaufman, L. and Rousseeuw, P.J.
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.XMeans, de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality.AbstractKMeansQualityMeasure
D. Pelleg, A. Moore
Proceedings of the 17th International Conference on Machine Learning (ICML 2000)
In: X-means: Extending K-means with Efficient Estimation on the Number of Clusters
http://www.pelleg.org/shared/hp/download/xmeans.ps
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.KMeansPlusPlusInitialMeans
D. Arthur, S. Vassilvitskii
k-means++: the advantages of careful seeding
In: Proc. of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2007
http://dx.doi.org/10.1145/1283383.1283494
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.PAMInitialMeans
Kaufman, L. and Rousseeuw, P.J.
Clustering my means of Medoids
In: Statistical Data Analysis Based on the L_1–Norm and Related Methods
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.quality.AbstractKMeansQualityMeasure, 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
http://dx.doi.org/10.1109/ICTAI.2008.154
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality.AkaikeInformationCriterion
H. Akaike
On entropy maximization principle
In: Application of statistics, 1977, North-Holland
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality.BayesianInformationCriterion
G. Schwarz
Estimating the dimension of a model
In: The annals of statistics 6.2
http://dx.doi.org/10.1214/aos/1176344136
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
M. Ankerst, M. Breunig, H.-P. Kriegel, and J. Sander
OPTICS: Ordering Points to Identify the Clustering Structure
In: Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD '99)
http://dx.doi.org/10.1145/304181.304187
de.lmu.ifi.dbs.elki.algorithm.clustering.optics.DeLiClu
E. Achtert, C. Böhm, P. Kröger
DeLiClu: Boosting Robustness, Completeness, Usability, and Efficiency of Hierarchical Clustering by a Closest Pair Ranking
In: Proc. 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006), Singapore, 2006
http://dx.doi.org/10.1007/11731139_16
de.lmu.ifi.dbs.elki.algorithm.clustering.optics.FastOPTICS, de.lmu.ifi.dbs.elki.index.preprocessed.fastoptics.RandomProjectedNeighborssAndDensities
Schneider, J., & Vlachos, M
Fast parameterless density-based clustering via random projections
In: Proc. 22nd ACM international conference on Conference on Information & Knowledge Management (CIKM)
http://dx.doi.org/10.1145/2505515.2505590
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
http://dx.doi.org/10.1145/276304.276314
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.DOC
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)
http://dx.doi.org/10.1145/564691.564739
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.DiSH
E. Achtert, C. Böhm, H.-P. Kriegel, P. Kröger, I. Müller-Gorman, A. Zimek
Detection and Visualization of Subspace Cluster Hierarchies
In: Proc. 12th International Conference on Database Systems for Advanced Applications (DASFAA), Bangkok, Thailand, 2007
http://dx.doi.org/10.1007/978-3-540-71703-4_15
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.HiSC
E. Achtert, C. Böhm, H.-P. Kriegel, P. Kröger, I. Müller-Gorman, A. Zimek
Finding Hierarchies of Subspace Clusters
In: Proc. 10th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'06), Berlin, Germany, 2006
http://www.dbs.ifi.lmu.de/Publikationen/Papers/PKDD06-HiSC.pdf
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)
http://dx.doi.org/10.1109/ICDM.2006.123
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)
http://dx.doi.org/10.1145/304181.304188
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.SUBCLU
K. Kailing, H.-P. Kriegel, P. Kröger
Density connected Subspace Clustering for High Dimensional Data
In: Proc. SIAM Int. Conf. on Data Mining (SDM'04), Lake Buena Vista, FL, 2004
http://www.siam.org/meetings/sdm04/proceedings/sdm04_023.pdf
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
http://dx.doi.org/10.1109/ICDMW.2007.40
de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.CenterOfMassMetaClustering, de.lmu.ifi.dbs.elki.application.AbstractApplication
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)
http://www.vldb.org/pvldb/vol8/p1976-schubert.pdf
de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.FDBSCAN
H.-P. Kriegel and M. Pfeifle
Density-based clustering of uncertain data
In: KDD05
http://dx.doi.org/10.1145/1081870.1081955
de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.FDBSCANNeighborPredicate
Hans-Peter Kriegel and Martin Pfeifle
Density-based clustering of uncertain data
In: Proc. 11th ACM Int. Conf. on Knowledge Discovery and Data Mining (KDD'05)
http://dx.doi.org/10.1145/1081870.1081955
de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.RepresentativeUncertainClustering
Andreas Züfle, Tobias Emrich, Klaus Arthur Schmid, Nikos Mamoulis, Arthur Zimek, Mathias Renz
Representative clustering of uncertain data
In: Proc. 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
http://dx.doi.org/10.1145/2623330.2623725
de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.UKMeans
M. Chau, R. Cheng, B. Kao, J. Ng
Uncertain data mining: An example in clustering location data
In: Proc. 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006)
http://dx.doi.org/10.1007/11731139_24
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), Santiago de Chile, Chile 1994
http://www.vldb.org/conf/1994/P487.PDF
de.lmu.ifi.dbs.elki.algorithm.itemsetmining.Eclat
M.J. Zaki, S. Parthasarathy, M. Ogihara, and W. Li
New Algorithms for Fast Discovery of Association Rules
In: Proc. 3rd ACM SIGKDD '97 Int. Conf. on Knowledge Discovery and Data Mining
http://www.aaai.org/Library/KDD/1997/kdd97-060.php
de.lmu.ifi.dbs.elki.algorithm.itemsetmining.FPGrowth
J. Han, J. Pei, Y. Yin
Mining frequent patterns without candidate generation
In: Proceedings of the 2000 ACM SIGMOD international conference on Management of data
http://dx.doi.org/10.1145/342009.335372
de.lmu.ifi.dbs.elki.algorithm.outlier.COP
Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek
Outlier Detection in Arbitrarily Oriented Subspaces
In: Proc. IEEE International Conference on Data Mining (ICDM 2012)
http://dx.doi.org/10.1109/ICDM.2012.21
de.lmu.ifi.dbs.elki.algorithm.outlier.DWOF
R. Momtaz, N. Mohssen, M. A. Gowayyed
DWOF: A Robust Density-Based Outlier Detection Approach
In: Pattern Recognition and Image Analysis, Proc. 6th Iberian Conference, IbPRIA 2013, Funchal, Madeira, Portugal, 2013.
http://dx.doi.org/10.1007%2F978-3-642-38628-2_61
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. of the Seventeenth International Conference on Machine Learning (ICML-2000)
de.lmu.ifi.dbs.elki.algorithm.outlier.OPTICSOF
M. M. Breunig, H.-P. Kriegel, R. Ng, and J. Sander
OPTICS-OF: Identifying Local Outliers
In: Proc. of the 3rd European Conference on Principles of Knowledge Discovery and Data Mining (PKDD), Prague, Czech Republic
http://springerlink.metapress.com/content/76bx6413gqb4tvta/
de.lmu.ifi.dbs.elki.algorithm.outlier.SimpleCOP
Arthur Zimek
Correlation Clustering. PhD thesis, Chapter 18
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
H.-P. Kriegel, M. Schubert, A. Zimek
Angle-Based Outlier Detection in High-dimensional Data
In: Proc. 14th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD '08), Las Vegas, NV, 2008
http://dx.doi.org/10.1145/1401890.1401946
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
http://dx.doi.org/10.1016%2F0377-0427%2887%2990125-7
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: Procs Int. Conf. on Very Large Databases (VLDB'98), New York, USA, 1998
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: Procs Int. Conf. on Very Large Databases (VLDB'98), New York, USA, 1998
de.lmu.ifi.dbs.elki.algorithm.outlier.distance.HilOut
F. Angiulli, C. Pizzuti
Fast Outlier Detection in High Dimensional Spaces
In: Proc. European Conference on Principles of Knowledge Discovery and Data Mining (PKDD'02)
http://dx.doi.org/10.1145/375663.375668
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. of the Int. Conf. on Management of Data, Dallas, Texas, 2000
http://dx.doi.org/10.1145/342009.335437
de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNWeightOutlier
F. Angiulli, C. Pizzuti
Fast Outlier Detection in High Dimensional Spaces
In: Proc. European Conference on Principles of Knowledge Discovery and Data Mining (PKDD'02), Helsinki, Finland, 2002
http://dx.doi.org/10.1007/3-540-45681-3_2
de.lmu.ifi.dbs.elki.algorithm.outlier.distance.ODIN, tutorial.outlier.ODIN
V. Hautamäki and I. Kärkkäinen and P Fränti
Outlier detection using k-nearest neighbour graph
In: Proc. 17th Int. Conf. Pattern Recognition, ICPR 2004
http://dx.doi.org/10.1109/ICPR.2004.1334558
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)
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