wiki:RelatedPublications

Publications implemented or referenced by ELKI

The following publications are cited by classes in ELKI (as of ELKI 0.7.1):

de.lmu.ifi.dbs.elki.algorithm.DependencyDerivator

By: 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.
Online: http://dx.doi.org/10.1145/1150402.1150408

de.lmu.ifi.dbs.elki.algorithm.clustering.CanopyPreClustering

By: 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
Online: 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

By: 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
Online: http://www.aaai.org/Papers/KDD/1996/KDD96-037

de.lmu.ifi.dbs.elki.algorithm.clustering.GriDBSCAN

By: S. Mahran and K. Mahar
Using grid for accelerating density-based clustering
In: 8th IEEE Int. Conf. on Computer and Information Technology
Online: http://dx.doi.org/10.1109/CIT.2008.4594646

de.lmu.ifi.dbs.elki.algorithm.clustering.NaiveMeanShiftClustering

By: Y. Cheng
Mean shift, mode seeking, and clustering
In: IEEE Transactions on Pattern Analysis and Machine Intelligence 17-8
Online: http://dx.doi.org/10.1109/34.400568

de.lmu.ifi.dbs.elki.algorithm.clustering.SNNClustering

By: 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
Online: http://www.siam.org/meetings/sdm03/proceedings/sdm03_05.pdf

de.lmu.ifi.dbs.elki.algorithm.clustering.affinitypropagation.AffinityPropagationClusteringAlgorithm

By: B. J. Frey and D. Dueck
Clustering by Passing Messages Between Data Points
In: Science Vol 315
Online: http://dx.doi.org/10.1126/science.1136800

de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering.ChengAndChurch

By: 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

By: 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
Online: 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

By: 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
Online: 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

By: 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
Online: 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

By: 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
Online: http://dx.doi.org/10.1145/1007568.1007620

de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.HiCO

By: 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
Online: http://dx.doi.org/10.1109/SSDBM.2006.35

de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.LMCLUS

By: Robert Haralick, Rave Harpaz
Linear manifold clustering in high dimensional spaces by stochastic search
In: Pattern Recognition volume 40, Issue 10
Online: http://dx.doi.org/10.1016/j.patcog.2007.01.020

de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.ORCLUS

By: 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)
Online: http://dx.doi.org/10.1145/342009.335383

de.lmu.ifi.dbs.elki.algorithm.clustering.em.EM

By: 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
Online: http://www.jstor.org/stable/2984875

de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.GeneralizedDBSCAN

By: 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
Online: http://dx.doi.org/10.1023/A:1009745219419

de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.LSDBC

By: E. Biçici and D. Yuret
Locally Scaled Density Based Clustering
In: Adaptive and Natural Computing Algorithms
Online: 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

By: 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
Online: http://dx.doi.org/10.1109/ICDM.2004.10087

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.AGNES

By: L. Kaufman and P. J. Rousseeuw
Agglomerative Nesting (Program AGNES)
In: Finding Groups in Data: An Introduction to Cluster Analysis
Online: http://dx.doi.org/10.1002/9780470316801.ch5

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.AGNES

By: P. H. Sneath
The application of computers to taxonomy
In: Journal of general microbiology, 17(1)
Online: 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

By: 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
Online: http://dx.doi.org/10.1007/978-3-642-37456-2_14

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.AnderbergHierarchicalClustering

By: M. R. Anderberg
Hierarchical Clustering Methods
In: Cluster Analysis for Applications

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.CLINK

By: D. Defays
An Efficient Algorithm for the Complete Link Cluster Method
In: The Computer Journal 20.4
Online: 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

By: 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

By: G. N. Lance and W. T. Williams
A general theory of classificatory sorting strategies 1. Hierarchical systems
In: The computer journal 9.4
Online: 10.1093/comjnl/9.4.373

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.MedianLinkageMethod

By: J. C. Gower
A comparison of some methods of cluster analysis
In: Biometrics (1967)
Online: http://www.jstor.org/stable/10.2307/2528417

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.SLINK

By: R. Sibson
SLINK: An optimally efficient algorithm for the single-link cluster method
In: The Computer Journal 16 (1973), No. 1, p. 30-34.
Online: http://dx.doi.org/10.1093/comjnl/16.1.30

de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.SingleLinkageMethod

By: 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

By: J. H. Ward Jr
Hierarchical grouping to optimize an objective function
In: Journal of the American statistical association 58.301
Online: http://dx.doi.org/10.1080/01621459.1963.10500845

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.CLARA

By: 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

By: 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.KMeansCompare,
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansSort

By: S. J. Phillips
Acceleration of k-means and related clustering algorithms
In: Proc. 4th Int. Workshop on Algorithm Engineering and Experiments (ALENEX 2002)
Online: http://dx.doi.org/10.1007/3-540-45643-0_13

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansElkan

By: 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

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansHamerly

By: G. Hamerly
Making k-means even faster
In: Proc. 2010 SIAM International Conference on Data Mining
Online: http://dx.doi.org/10.1137/1.9781611972801.12

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansLloyd

By: S. Lloyd
Least squares quantization in PCM
In: IEEE Transactions on Information Theory 28 (2): 129–137.
Online: http://dx.doi.org/10.1109/TIT.1982.1056489

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansMacQueen

By: J. MacQueen
Some Methods for Classification and Analysis of Multivariate Observations
In: 5th Berkeley Symp. Math. Statist. Prob., Vol. 1, 1967, pp 281-297
Online: http://projecteuclid.org/euclid.bsmsp/1200512992

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMediansLloyd

By: 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.pdf

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPAM

By: 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

By: 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
Online: http://www.pelleg.org/shared/hp/download/xmeans.ps

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.KMeansPlusPlusInitialMeans

By: 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
Online: http://dx.doi.org/10.1145/1283383.1283494

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.initialization.PAMInitialMeans

By: 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

By: 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

By: Q. Zhao, M. Xu, P. Fränti
Knee Point Detection on Bayesian Information Criterion
In: 20th IEEE International Conference on Tools with Artificial Intelligence
Online: http://dx.doi.org/10.1109/ICTAI.2008.154

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality.AkaikeInformationCriterion

By: H. Akaike
On entropy maximization principle
In: Application of statistics, 1977, North-Holland

de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.quality.BayesianInformationCriterion

By: G. Schwarz
Estimating the dimension of a model
In: The annals of statistics 6.2
Online: 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

By: 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)
Online: http://dx.doi.org/10.1145/304181.304187

de.lmu.ifi.dbs.elki.algorithm.clustering.optics.DeLiClu

By: 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
Online: 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.RandomProjectedNeighborsAndDensities

By: J. Schneider and M. Vlachos
Fast parameterless density-based clustering via random projections
In: Proc. 22nd ACM international conference on Conference on Information & Knowledge Management (CIKM)
Online: http://dx.doi.org/10.1145/2505515.2505590

de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.CLIQUE

By: 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
Online: http://dx.doi.org/10.1145/276304.276314

de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.DOC

By: 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)
Online: http://dx.doi.org/10.1145/564691.564739

de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.DiSH

By: 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
Online: http://dx.doi.org/10.1007/978-3-540-71703-4_15

de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.HiSC

By: 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
Online: http://www.dbs.ifi.lmu.de/Publikationen/Papers/PKDD06-HiSC.pdf

de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.P3C

By: Gabriela Moise, Jörg Sander, Martin Ester
P3C: A Robust Projected Clustering Algorithm
In: Proc. Sixth International Conference on Data Mining (ICDM '06)
Online: http://dx.doi.org/10.1109/ICDM.2006.123

de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.PROCLUS

By: 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)
Online: http://dx.doi.org/10.1145/304181.304188

de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.SUBCLU

By: 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
Online: http://www.siam.org/meetings/sdm04/proceedings/sdm04_023.pdf

de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.CKMeans

By: S. D. Lee, B. Kao, R. Cheng
Reducing UK-means to K-means
In: ICDM Data Mining Workshops, 2007
Online: 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

By: 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

de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.FDBSCAN

By: H.-P. Kriegel and M. Pfeifle
Density-based clustering of uncertain data
In: KDD05
Online: http://dx.doi.org/10.1145/1081870.1081955

de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.FDBSCANNeighborPredicate

By: 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)
Online: http://dx.doi.org/10.1145/1081870.1081955

de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.RepresentativeUncertainClustering

By: 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
Online: http://dx.doi.org/10.1145/2623330.2623725

de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.UKMeans

By: 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)
Online: http://dx.doi.org/10.1007/11731139_24

de.lmu.ifi.dbs.elki.algorithm.itemsetmining.APRIORI

By: 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
Online: http://www.vldb.org/conf/1994/P487.PDF

de.lmu.ifi.dbs.elki.algorithm.itemsetmining.Eclat

By: 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
Online: http://www.aaai.org/Library/KDD/1997/kdd97-060.php

de.lmu.ifi.dbs.elki.algorithm.itemsetmining.FPGrowth

By: 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
Online: http://dx.doi.org/10.1145/342009.335372

de.lmu.ifi.dbs.elki.algorithm.outlier.COP,
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.outlier.COPVectorVisualization

By: 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)
Online: http://dx.doi.org/10.1109/ICDM.2012.21

de.lmu.ifi.dbs.elki.algorithm.outlier.DWOF

By: 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.
Online: 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
By: 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

By: 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
Online: http://springerlink.metapress.com/content/76bx6413gqb4tvta/

de.lmu.ifi.dbs.elki.algorithm.outlier.SimpleCOP

By: Arthur Zimek
Correlation Clustering
In: 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

By: 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
Online: 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

By: P. J. Rousseeuw
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
In: Journal of Computational and Applied Mathematics, Volume 20
Online: 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

By: 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
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Algorithms for Mining Distance-Based Outliers in Large Datasets
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de.lmu.ifi.dbs.elki.algorithm.outlier.distance.HilOut

By: F. Angiulli, C. Pizzuti
Fast Outlier Detection in High Dimensional Spaces
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de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNOutlier

By: S. Ramaswamy, and R. Rastogi, and K. Shim
Efficient Algorithms for Mining Outliers from Large Data Sets
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de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNWeightOutlier

By: F. Angiulli, C. Pizzuti
Fast Outlier Detection in High Dimensional Spaces
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Online: http://dx.doi.org/10.1007/3-540-45681-3_2

de.lmu.ifi.dbs.elki.algorithm.outlier.distance.LocalIsolationCoefficient

By: B. Yu, and M. Song, and L. Wang
Local Isolation Coefficient-Based Outlier Mining Algorithm
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de.lmu.ifi.dbs.elki.algorithm.outlier.distance.ODIN

By: V. Hautamäki and I. Kärkkäinen and P. Fränti
Outlier detection using k-nearest neighbour graph
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de.lmu.ifi.dbs.elki.algorithm.outlier.distance.ReferenceBasedOutlierDetection

By: Y. Pei, O.R. Zaiane, Y. Gao
An Efficient Reference-based Approach to Outlier Detection in Large Datasets
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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

By: E. Schubert, A. Zimek, H.-P. Kriegel
Local Outlier Detection Reconsidered: a Generalized View on Locality with Applications to Spatial, Video, and Network Outlier Detection
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de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.IDOS

By: Jonathan von Brünken, Michael E. Houle, Arthur Zimek
Intrinsic Dimensional Outlier Detection in High-Dimensional Data
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de.lmu.ifi.dbs.elki.algorithm.outlier.lof.ALOCI,
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LOCI

By: S. Papadimitriou, H. Kitagawa, P. B. Gibbons, C. Faloutsos
LOCI: Fast Outlier Detection Using the Local Correlation Integral
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de.lmu.ifi.dbs.elki.algorithm.outlier.lof.COF

By: J. Tang, Z. Chen, A. W. C. Fu, D. W. Cheung
Enhancing effectiveness of outlier detections for low density patterns
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de.lmu.ifi.dbs.elki.algorithm.outlier.lof.FlexibleLOF

By: M. M. Breunig, H.-P. Kriegel, R. Ng, J. Sander
LOF: Identifying Density-Based Local Outliers
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de.lmu.ifi.dbs.elki.algorithm.outlier.lof.INFLO

By: W. Jin, A. Tung, J. Han, and W. Wang
Ranking outliers using symmetric neighborhood relationship
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de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS

By: Erich Schubert, Arthur Zimek, Hans-Peter Kriegel
Generalized Outlier Detection with Flexible Kernel Density Estimates
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de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDF

By: L. J. Latecki, A. Lazarevic, D. Pokrajac
Outlier Detection with Kernel Density Functions
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de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDOF

By: K. Zhang, M. Hutter, H. Jin
A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data
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Online: http://dx.doi.org/10.1007/978-3-642-01307-2_84

de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LOF

By: M. M. Breunig, H.-P. Kriegel, R. Ng, and J. Sander
LOF: Identifying Density-Based Local Outliers
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de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LoOP

By: H.-P. Kriegel, P. Kröger, E. Schubert, A. Zimek
LoOP: Local Outlier Probabilities
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de.lmu.ifi.dbs.elki.algorithm.outlier.lof.VarianceOfVolume

By: T. Hu, and S. Y. Sung
Detecting pattern-based outliers
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de.lmu.ifi.dbs.elki.algorithm.outlier.meta.FeatureBagging

By: A. Lazarevic, V. Kumar
Feature Bagging for Outlier Detection
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de.lmu.ifi.dbs.elki.algorithm.outlier.meta.HiCS

By: Fabian Keller, Emmanuel Müller, Klemens Böhm
HiCS: High Contrast Subspaces for Density-Based Outlier Ranking
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de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuGLSBackwardSearchAlgorithm

By: F. Chen and C.-T. Lu and A. P. Boedihardjo
GLS-SOD: A Generalized Local Statistical Approach for Spatial Outlier Detection
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de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuMeanMultipleAttributes,
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuMedianMultipleAttributes

By: Chang-Tien Lu and Dechang Chen and Yufeng Kou
Detecting Spatial Outliers with Multiple Attributes
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de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuMedianAlgorithm

By: C.-T. Lu and D. Chen and Y. Kou
Algorithms for Spatial Outlier Detection
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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

By: S. Shekhar and C.-T. Lu and P. Zhang
A Unified Approach to Detecting Spatial Outliers
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de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuRandomWalkEC

By: X. Liu and C.-T. Lu and F. Chen
Spatial outlier detection: random walk based approaches
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de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.SLOM

By: Sanjay Chawla and Pei Sun
SLOM: a new measure for local spatial outliers
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de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.SOF

By: Huang, T., Qin, X.
Detecting outliers in spatial database
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de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.TrimmedMeanApproach

By: Tianming Hu and Sam Yuan Sung
A trimmed mean approach to finding spatial outliers
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de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.AbstractAggarwalYuOutlier,
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de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.AggarwalYuNaive

By: C.C. Aggarwal, P. S. Yu
Outlier detection for high dimensional data
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de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.OUTRES

By: E. Müller, M. Schiffer, T. Seidl
Adaptive outlierness for subspace outlier ranking
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de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.OutRankS1

By: Emmanuel Müller, Ira Assent, Uwe Steinhausen, Thomas Seidl
OutRank: ranking outliers in high dimensional data
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de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.SOD

By: H.-P. Kriegel, P. Kröger, E. Schubert, A. Zimek
Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data
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de.lmu.ifi.dbs.elki.algorithm.outlier.svm.LibSVMOneClassOutlierDetection

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de.lmu.ifi.dbs.elki.algorithm.statistics.HopkinsStatisticClusteringTendency

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A new method for determining the type of distribution of plant individuals
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de.lmu.ifi.dbs.elki.application.greedyensemble.ComputeKNNOutlierScores

By: E. Schubert, R. Wojdanowski, A. Zimek, H.-P. Kriegel
On Evaluation of Outlier Rankings and Outlier Scores
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de.lmu.ifi.dbs.elki.data.uncertain.UnweightedDiscreteUncertainObject

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Probabilistic databases: diamonds in the dirt
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de.lmu.ifi.dbs.elki.data.uncertain.WeightedDiscreteUncertainObject

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de.lmu.ifi.dbs.elki.datasource.filter.transform.LinearDiscriminantAnalysisFilter

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de.lmu.ifi.dbs.elki.datasource.filter.transform.PerturbationFilter

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de.lmu.ifi.dbs.elki.distance.distancefunction.BrayCurtisDistanceFunction

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de.lmu.ifi.dbs.elki.distance.distancefunction.BrayCurtisDistanceFunction

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de.lmu.ifi.dbs.elki.distance.distancefunction.BrayCurtisDistanceFunction

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de.lmu.ifi.dbs.elki.distance.distancefunction.CanberraDistanceFunction

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de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram.RGBHistogramQuadraticDistanceFunction

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de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.KullbackLeiblerDivergenceAsymmetricDistanceFunction,
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de.lmu.ifi.dbs.elki.evaluation.clustering.SetMatchingPurity

By: Zhao, Y. and Karypis, G.
Criterion functions for document clustering: Experiments and analysis
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de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateCIndex

By: L. J. Hubert and J. R. Levin
A general statistical framework for assessing categorical clustering in free recall.
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de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateConcordantPairs

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de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateConcordantPairs

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de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateDaviesBouldin

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A Cluster Separation Measure
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de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluatePBMIndex

By: M. K. Pakhira, and S. Bandyopadhyay, and U. Maulik
Validity index for crisp and fuzzy clusters
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de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateVarianceRatioCriteria

By: R. B. Calinski and J. Harabasz
A dendrite method for cluster analysis
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de.lmu.ifi.dbs.elki.evaluation.clustering.pairsegments.ClusterPairSegmentAnalysis,
de.lmu.ifi.dbs.elki.evaluation.clustering.pairsegments.Segments,
de.lmu.ifi.dbs.elki.visualization.visualizers.pairsegments.CircleSegmentsVisualizer

By: Elke Achtert, Sascha Goldhofer, Hans-Peter Kriegel, Erich Schubert, Arthur Zimek
Evaluation of Clusterings – Metrics and Visual Support
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de.lmu.ifi.dbs.elki.evaluation.outlier.OutlierSmROCCurve

By: W. Klement, P. A. Flach, N. Japkowicz, S. Matwin
Smooth Receiver Operating Characteristics (smROC) Curves
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de.lmu.ifi.dbs.elki.index.idistance.InMemoryIDistanceIndex

By: C. Yu, B. C. Ooi, K. L. Tan, H. V. Jagadish
Indexing the distance: An efficient method to knn processing
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de.lmu.ifi.dbs.elki.index.idistance.InMemoryIDistanceIndex

By: H. V. Jagadish, B. C. Ooi, K. L. Tan, C. Yu, R. Zhang
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de.lmu.ifi.dbs.elki.index.lsh.hashfamilies.CosineHashFunctionFamily,
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Similarity estimation techniques from rounding algorithms
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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

By: M. Datar and N. Immorlica and P. Indyk and V. S. Mirrokni
Locality-sensitive hashing scheme based on p-stable distributions
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de.lmu.ifi.dbs.elki.index.preprocessed.knn.NaiveProjectedKNNPreprocessor,
de.lmu.ifi.dbs.elki.index.preprocessed.knn.SpacefillingKNNPreprocessor,
de.lmu.ifi.dbs.elki.index.preprocessed.knn.SpacefillingMaterializeKNNPreprocessor

By: E. Schubert, A. Zimek, H.-P. Kriegel
Fast and Scalable Outlier Detection with Approximate Nearest Neighbor Ensembles
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de.lmu.ifi.dbs.elki.index.preprocessed.knn.RandomSampleKNNPreprocessor

By: A. Zimek and M. Gaudet and R. J. G. B. Campello and J. Sander
Subsampling for Efficient and Effective Unsupervised Outlier Detection Ensembles
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de.lmu.ifi.dbs.elki.index.projected.PINN

By: T. de Vries, S. Chawla, M. E. Houle
Finding local anomalies in very high dimensional space
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de.lmu.ifi.dbs.elki.index.tree.metrical.covertree.CoverTree

By: A. Beygelzimer, S. Kakade, J. Langford
Cover trees for nearest neighbor
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de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree.MTree,
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de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.MLBDistSplit,
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.MMRadSplit,
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.MRadSplit,
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.RandomSplit

By: P. Ciaccia, M. Patella, P. Zezula
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
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de.lmu.ifi.dbs.elki.index.tree.spatial.kd.MinimalisticMemoryKDTree,
de.lmu.ifi.dbs.elki.index.tree.spatial.kd.SmallMemoryKDTree,
de.lmu.ifi.dbs.elki.math.spacefillingcurves.BinarySplitSpatialSorter

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Multidimensional binary search trees used for associative searching
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de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query.EuclideanRStarTreeKNNQuery,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query.RStarTreeKNNQuery

By: G. R. Hjaltason, H. Samet
Ranking in spatial databases
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de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query.EuclideanRStarTreeRangeQuery,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query.RStarTreeRangeQuery

By: J. Kuan, P. Lewis
Fast k nearest neighbour search for R-tree family
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de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.rstar.RStarTree,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.insert.ApproximativeLeastOverlapInsertionStrategy,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.insert.CombinedInsertionStrategy,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.insert.LeastEnlargementWithAreaInsertionStrategy,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.insert.LeastOverlapInsertionStrategy,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.overflow.LimitedReinsertOverflowTreatment,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.reinsert.CloseReinsert,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.reinsert.FarReinsert,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.split.TopologicalSplitter

By: N. Beckmann, H.-P. Kriegel, R. Schneider, B. Seeger
The R*-tree: an efficient and robust access method for points and rectangles
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de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.bulk.OneDimSortBulkSplit

By: Roussopoulos, N. and Leifker, D.
Direct spatial search on pictorial databases using packed R-trees
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de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.bulk.SortTileRecursiveBulkSplit

By: Leutenegger, S.T. and Lopez, M.A. and Edgington, J.
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de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.bulk.SpatialSortBulkSplit

By: Kamel, I. and Faloutsos, C.
On packing R-trees
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de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.insert.LeastEnlargementInsertionStrategy,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.split.RTreeLinearSplit,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.split.RTreeQuadraticSplit

By: Antonin Guttman
R-Trees: A Dynamic Index Structure For Spatial Searching
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de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.split.AngTanLinearSplit

By: C. H. Ang and T. C. Tan
New linear node splitting algorithm for R-trees
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de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.split.GreeneSplit

By: Diane Greene
An implementation and performance analysis of spatial data access methods
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de.lmu.ifi.dbs.elki.index.vafile.DAFile,
de.lmu.ifi.dbs.elki.index.vafile.PartialVAFile

By: Hans-Peter Kriegel, Peer Kröger, Matthias Schubert, Ziyue Zhu
Efficient Query Processing in Arbitrary Subspaces Using Vector Approximations
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de.lmu.ifi.dbs.elki.index.vafile.VAFile

By: Weber, R. and Blott, S.
An approximation based data structure for similarity search
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de.lmu.ifi.dbs.elki.math.Mean,
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Note on a method for calculating corrected sums of squares and products
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de.lmu.ifi.dbs.elki.math.MeanVariance

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Updating Mean and Variance Estimates: An Improved Method
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de.lmu.ifi.dbs.elki.math.StatisticalMoments

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Computing Higher-Order Moments Online
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de.lmu.ifi.dbs.elki.math.dimensionsimilarity.HSMDimensionSimilarity,
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By: A. Tatu, G. Albuquerque, M. Eisemann, P. Bak, H. Theisel, M. A. Magnor, and D. A. Keim
Automated Analytical Methods to Support Visual Exploration of High-Dimensional Data
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de.lmu.ifi.dbs.elki.math.dimensionsimilarity.HiCSDimensionSimilarity,
de.lmu.ifi.dbs.elki.math.dimensionsimilarity.SURFINGDimensionSimilarity,
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de.lmu.ifi.dbs.elki.math.dimensionsimilarity.SlopeInversionDimensionSimilarity,
de.lmu.ifi.dbs.elki.math.statistics.dependence.HiCSDependenceMeasure,
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de.lmu.ifi.dbs.elki.math.statistics.dependence.SlopeDependenceMeasure,
de.lmu.ifi.dbs.elki.math.statistics.dependence.SlopeInversionDependenceMeasure

By: Elke Achtert, Hans-Peter Kriegel, Erich Schubert, Arthur Zimek
Interactive Data Mining with 3D-Parallel-Coordinate-Trees
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de.lmu.ifi.dbs.elki.math.dimensionsimilarity.MCEDimensionSimilarity,
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By: D. Guo
Coordinating computational and visual approaches for interactive feature selection and multivariate clustering
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de.lmu.ifi.dbs.elki.math.dimensionsimilarity.SURFINGDimensionSimilarity,
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By: Christian Baumgartner, Claudia Plant, Karin Kailing, Hans-Peter Kriegel, and Peer Kröger
Subspace Selection for Clustering High-Dimensional Data
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de.lmu.ifi.dbs.elki.math.geodesy.SphereUtil

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Aviation Formulary
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de.lmu.ifi.dbs.elki.math.geodesy.SphereUtil

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Direct and inverse solutions of geodesics on the ellipsoid with application of nested equations
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de.lmu.ifi.dbs.elki.math.geodesy.SphereUtil

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Virtues of the Haversine
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de.lmu.ifi.dbs.elki.math.geometry.GrahamScanConvexHull2D

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An Efficient Algorithm for Determining the Convex Hull of a Finite Planar Set
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de.lmu.ifi.dbs.elki.math.geometry.PrimsMinimumSpanningTree

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Shortest connection networks and some generalizations
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de.lmu.ifi.dbs.elki.math.geometry.SweepHullDelaunay2D

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S-hull: a fast sweep-hull routine for Delaunay triangulation
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de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCAFilteredAutotuningRunner,
de.lmu.ifi.dbs.elki.math.linearalgebra.pca.WeightedCovarianceMatrixBuilder

By: Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek
A General Framework for Increasing the Robustness of PCA-based Correlation Clustering Algorithms
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de.lmu.ifi.dbs.elki.math.linearalgebra.pca.RANSACCovarianceMatrixBuilder,
de.lmu.ifi.dbs.elki.utilities.scaling.outlier.COPOutlierScaling

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Outlier Detection in Arbitrarily Oriented Subspaces
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de.lmu.ifi.dbs.elki.math.linearalgebra.pca.RANSACCovarianceMatrixBuilder

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Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
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de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections.AchlioptasRandomProjectionFamily

By: D. Achlioptas
Database-friendly random projections: Johnson-Lindenstrauss with binary coins
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de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections.CauchyRandomProjectionFamily,
de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections.GaussianRandomProjectionFamily

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Locality-sensitive hashing scheme based on p-stable distributions
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de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections.RandomSubsetProjectionFamily

By: L. Breiman
Bagging predictors
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de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections.SimplifiedRandomHyperplaneProjectionFamily

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Finding near-duplicate web pages: a large-scale evaluation of algorithms
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de.lmu.ifi.dbs.elki.math.random.XorShift1024NonThreadsafeRandom,
de.lmu.ifi.dbs.elki.math.random.XorShift64NonThreadsafeRandom

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An experimental exploration of Marsaglia's xorshift generators, scrambled
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de.lmu.ifi.dbs.elki.math.spacefillingcurves.HilbertSpatialSorter

By: D. Hilbert
Über die stetige Abbildung einer Linie auf ein Flächenstück
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de.lmu.ifi.dbs.elki.math.spacefillingcurves.PeanoSpatialSorter

By: G. Peano
Sur une courbe, qui remplit toute une aire plane
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de.lmu.ifi.dbs.elki.math.statistics.ProbabilityWeightedMoments,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.GeneralizedExtremeValueLMMEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.GeneralizedParetoLMMEstimator

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de.lmu.ifi.dbs.elki.math.statistics.dependence.DistanceCorrelationDependenceMeasure

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Measuring and testing dependence by correlation of distances
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de.lmu.ifi.dbs.elki.math.statistics.dependence.HoeffdingsDDependenceMeasure

By: W. Hoeffding
A non-parametric test of independence
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de.lmu.ifi.dbs.elki.math.statistics.distribution.ChiSquaredDistribution,
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de.lmu.ifi.dbs.elki.math.statistics.distribution.GammaDistribution

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Algorithm AS 103: Psi (Digamma) Function
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de.lmu.ifi.dbs.elki.math.statistics.distribution.HaltonUniformDistribution

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Randomized halton sequences
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de.lmu.ifi.dbs.elki.math.statistics.distribution.PoissonDistribution

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Fast and accurate computation of binomial probabilities
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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

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Applied Robust Statistics
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de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.EMGOlivierNorbergEstimator

By: J. Olivier, M. M. Norberg
Positively skewed data: Revisiting the Box-Cox power transformation
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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

By: J.R.M. Hosking
Fortran routines for use with the method of L-moments Version 3.03
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de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.ExponentialMedianEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LogisticMADEstimator

By: D. J. Olive
Robust Estimators for Transformed Location Scale Families

de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.GammaChoiWetteEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LogGammaChoiWetteEstimator

By: S. C. Choi, R. Wette
Maximum likelihood estimation of the parameters of the gamma distribution and their bias
In: Technometrics
Online: http://www.jstor.org/stable/10.2307/1266892

de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.GammaMADEstimator

By: J. Chen. H. Rubin
Bounds for the difference between median and mean of Gamma and Poisson distributions
In: Statist. Probab. Lett., 4

de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.GammaMOMEstimator

By: G. Casella, R. L. Berger
Statistical inference. Vol. 70
In: Statistical inference. Vol. 70

de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LaplaceMLEEstimator

By: R. M. Norton
The Double Exponential Distribution: Using Calculus to Find a Maximum Likelihood Estimator
In: The American Statistician 38 (2)
Online: http://dx.doi.org/10.2307/2683252

de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LogNormalBilkovaLMMEstimator

By: 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

By: 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
Online: http://www.jstor.org/stable/10.2307/2285666

de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.meta.WinsorisingEstimator

By: 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)
Online: http://dx.doi.org/10.1214/aoms/1177730388

de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.AggregatedHillEstimator

By: R. Huisman and K. G. Koedijk and C. J. M. Kool and F. Palm
Tail-Index Estimates in Small Samples
In: Journal of Business & Economic Statistics
Online: http://dx.doi.org/10.1198/073500101316970421

de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.GEDEstimator

By: M. E. Houle, H. Kashima, M. Nett
Generalized expansion dimension
In: 12th International Conference on Data Mining Workshops (ICDMW)
Online: http://dx.doi.org/10.1109/ICDMW.2012.94

de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.HillEstimator

By: B. M. Hill
A simple general approach to inference about the tail of a distribution
In: The annals of statistics 3(5)
Online: http://dx.doi.org/10.1214/aos/1176343247

de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.MOMEstimator,
de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.RVEstimator

By: L. Amsaleg and O. Chelly and T. Furon and S. Girard and M. E. Houle and K. Kawarabayashi and M. Nett
Estimating Local Intrinsic Dimensionality
In: Proc. SIGKDD International Conference on Knowledge Discovery and Data Mining 2015
Online: http://dx.doi.org/10.1145/2783258.2783405

de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.ZipfEstimator

By: M. Kratz and S. I. Resnick
On Least Squares Estimates of an Exponential Tail Coefficient
In: Statistics & Risk Modeling. Band 14, Heft 4
Online: http://dx.doi.org/10.1524/strm.1996.14.4.353

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

By: J.S. Marron, D. Nolan
Canonical kernels for density estimation
In: Statistics & Probability Letters, Volume 7, Issue 3
Online: http://dx.doi.org/10.1016/0167-7152(88)90050-8

de.lmu.ifi.dbs.elki.math.statistics.tests.AndersonDarlingTest

By: T. W. Anderson, and D. A. Darling
Asymptotic theory of certain 'goodness of fit' criteria based on stochastic processes
In: Annals of mathematical statistics 23(2)
Online: http://dx.doi.org/10.1214/aoms/1177729437

de.lmu.ifi.dbs.elki.math.statistics.tests.AndersonDarlingTest

By: M. A. Stephens
EDF Statistics for Goodness of Fit and Some Comparisons
In: Journal of the American Statistical Association, Volume 69, Issue 347
Online: http://dx.doi.org/10.1080/01621459.1974.10480196

de.lmu.ifi.dbs.elki.math.statistics.tests.StandardizedTwoSampleAndersonDarlingTest

By: A. N. Pettitt
A two-sample Anderson-Darling rank statistic
In: Biometrika 63 (1)
Online: http://dx.doi.org/10.1093/biomet/63.1.161

de.lmu.ifi.dbs.elki.math.statistics.tests.StandardizedTwoSampleAndersonDarlingTest

By: F. W. Scholz, and M. A. Stephens
K-sample Anderson–Darling tests
In: Journal of the American Statistical Association, 82(399)
Online: http://dx.doi.org/10.1080/01621459.1987.10478517

de.lmu.ifi.dbs.elki.math.statistics.tests.StandardizedTwoSampleAndersonDarlingTest

By: D. A. Darling
The Kolmogorov-Smirnov, Cramer-von Mises tests
In: Annals of mathematical statistics 28(4)
Online: http://dx.doi.org/10.1214/aoms/1177706788

de.lmu.ifi.dbs.elki.result.KMLOutputHandler

By: E. Achtert, A. Hettab, H.-P. Kriegel, E. Schubert, A. Zimek
Spatial Outlier Detection: Data, Algorithms, Visualizations
In: Proc. 12th International Symposium on Spatial and Temporal Databases (SSTD), Minneapolis, MN, 2011
Online: http://dx.doi.org/10.1007/978-3-642-22922-0_41

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

By: R. Sedgewick
1.3 Union-Find Algorithms
In: Algorithms in C, Parts 1-4

de.lmu.ifi.dbs.elki.utilities.scaling.outlier.COPOutlierScaling

By: H.-P. Kriegel, P. Kröger, E. Schubert, A. Zimek
Interpreting and Unifying Outlier Scores
In: Proc. 11th SIAM International Conference on Data Mining (SDM), Mesa, AZ, 2011
Online: http://siam.omnibooksonline.com/2011datamining/data/papers/018.pdf

de.lmu.ifi.dbs.elki.utilities.scaling.outlier.HeDESNormalizationOutlierScaling

By: H. V. Nguyen, H. H. Ang, V. Gopalkrishnan
Mining Outliers with Ensemble of Heterogeneous Detectors on Random Subspaces
In: Proc. 15th International Conference on Database Systems for Advanced Applications (DASFAA 2010)
Online: http://dx.doi.org/10.1007/978-3-642-12026-8_29

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

By: H.-P. Kriegel, P. Kröger, E. Schubert, A. Zimek
Interpreting and Unifying Outlier Scores
In: Proc. 11th SIAM International Conference on Data Mining (SDM), Mesa, AZ, 2011
Online: http://dx.doi.org/10.1137/1.9781611972818.2

de.lmu.ifi.dbs.elki.utilities.scaling.outlier.MixtureModelOutlierScalingFunction,
de.lmu.ifi.dbs.elki.utilities.scaling.outlier.SigmoidOutlierScalingFunction

By: 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.
Online: http://dx.doi.org/10.1109/ICDM.2006.43

de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.density.DensityEstimationOverlay$Instance

By: D. W. Scott
Multivariate density estimation: Theory, Practice, and Visualization
In: Multivariate Density Estimation: Theory, Practice, and Visualization
Online: http://dx.doi.org/10.1002/9780470316849

de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.outlier.BubbleVisualization

By: E. Achtert, H.-P. Kriegel, L. Reichert, E. Schubert, R. Wojdanowski, A. Zimek
Visual Evaluation of Outlier Detection Models
In: Proceedings of the 15th International Conference on Database Systems for Advanced Applications (DASFAA), Tsukuba, Japan, 2010
Online: http://dx.doi.org/10.1007/978-3-642-12098-5_34

Last modified 5 months ago Last modified on Feb 12, 2016, 10:53:24 AM