-
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.
-
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
-
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
-
de.lmu.ifi.dbs.elki.algorithm.clustering.GriDBSCAN
-
S. Mahran and K. Mahar
Using grid for accelerating density-based clustering
In: 8th IEEE Int. Conf. on Computer and Information Technology
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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)
-
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
-
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
-
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
-
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
-
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
-
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)
-
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
-
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
-
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
-
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.MedianLinkageMethod
-
J. C. Gower
A comparison of some methods of cluster analysis
In: Biometrics (1967)
-
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.
-
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
-
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.KMeansCompare,
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansSort
-
S. J. Phillips
Acceleration of k-means and related clustering algorithms
In: Proc. 4th Int. Workshop on Algorithm Engineering and Experiments (ALENEX 2002)
-
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
-
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
-
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.
-
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
-
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
-
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
-
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
-
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
-
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
-
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)
-
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
-
de.lmu.ifi.dbs.elki.algorithm.clustering.optics.FastOPTICS,
de.lmu.ifi.dbs.elki.index.preprocessed.fastoptics.RandomProjectedNeighborsAndDensities
-
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)
-
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
-
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)
-
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
-
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
-
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)
-
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)
-
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
-
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
-
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)
-
de.lmu.ifi.dbs.elki.algorithm.clustering.uncertain.FDBSCAN
-
H.-P. Kriegel and M. Pfeifle
Density-based clustering of uncertain data
In: KDD05
-
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)
-
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
-
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)
-
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
-
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
-
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
-
de.lmu.ifi.dbs.elki.algorithm.outlier.COP,
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.outlier.COPVectorVisualization
-
Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek
Outlier Detection in Arbitrarily Oriented Subspaces
In: Proc. IEEE International Conference on Data Mining (ICDM 2012)
-
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.
-
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
-
de.lmu.ifi.dbs.elki.algorithm.outlier.SimpleCOP
-
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
-
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
-
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
-
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)
-
de.lmu.ifi.dbs.elki.algorithm.outlier.distance.KNNOutlier
-
S. Ramaswamy, and R. Rastogi, and K. Shim
Efficient Algorithms for Mining Outliers from Large Data Sets
In: Proc. Int. Conf. on Management of Data, 2000
-
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
-
de.lmu.ifi.dbs.elki.algorithm.outlier.distance.LocalIsolationCoefficient
-
B. Yu, and M. Song, and L. Wang
Local Isolation Coefficient-Based Outlier Mining Algorithm
In: Int. Conf. on Information Technology and Computer Science (ITCS) 2009
-
de.lmu.ifi.dbs.elki.algorithm.outlier.distance.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
-
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)
-
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
-
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
In: Data Mining and Knowledge Discovery, 28(1): 190–237, 2014.
-
de.lmu.ifi.dbs.elki.algorithm.outlier.intrinsic.IDOS
-
Jonathan von Brünken, Michael E. Houle, Arthur Zimek
Intrinsic Dimensional Outlier Detection in High-Dimensional Data
In: NII Technical Report (NII-2015-003E)
-
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.ALOCI,
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LOCI
-
S. Papadimitriou, H. Kitagawa, P. B. Gibbons, C. Faloutsos
LOCI: Fast Outlier Detection Using the Local Correlation Integral
In: Proc. 19th IEEE Int. Conf. on Data Engineering (ICDE '03)
-
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.COF
-
J. Tang, Z. Chen, A. W. C. Fu, D. W. Cheung
Enhancing effectiveness of outlier detections for low density patterns
In: In Advances in Knowledge Discovery and Data Mining
-
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.FlexibleLOF
-
M. M. Breunig, H.-P. Kriegel, R. Ng, J. Sander
LOF: Identifying Density-Based Local Outliers
In: Proc. 2nd ACM SIGMOD Int. Conf. on Management of Data (SIGMOD '00), Dallas, TX, 2000
-
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.INFLO
-
W. Jin, A. Tung, J. Han, and W. Wang
Ranking outliers using symmetric neighborhood relationship
In: Proc. 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
-
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.KDEOS
-
Erich Schubert, Arthur Zimek, Hans-Peter Kriegel
Generalized Outlier Detection with Flexible Kernel Density Estimates
In: Proc. 14th SIAM International Conference on Data Mining (SDM), Philadelphia, PA, 2014
-
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDF
-
L. J. Latecki, A. Lazarevic, D. Pokrajac
Outlier Detection with Kernel Density Functions
In: Machine Learning and Data Mining in Pattern Recognition
-
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LDOF
-
K. Zhang, M. Hutter, H. Jin
A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data
In: Proc. 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 2009)
-
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LOF
-
M. M. Breunig, H.-P. Kriegel, R. Ng, and J. Sander
LOF: Identifying Density-Based Local Outliers
In: Proc. 2nd ACM SIGMOD Int. Conf. on Management of Data (SIGMOD '00), Dallas, TX, 2000
-
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LoOP
-
H.-P. Kriegel, P. Kröger, E. Schubert, A. Zimek
LoOP: Local Outlier Probabilities
In: Proceedings of the 18th International Conference on Information and Knowledge Management (CIKM), Hong Kong, China, 2009
-
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.VarianceOfVolume
-
T. Hu, and S. Y. Sung
Detecting pattern-based outliers
In: Pattern Recognition Letters 24(16)
-
de.lmu.ifi.dbs.elki.algorithm.outlier.meta.FeatureBagging
-
A. Lazarevic, V. Kumar
Feature Bagging for Outlier Detection
In: Proc. of the 11th ACM SIGKDD international conference on Knowledge discovery in data mining
-
de.lmu.ifi.dbs.elki.algorithm.outlier.meta.HiCS
-
Fabian Keller, Emmanuel Müller, Klemens Böhm
HiCS: High Contrast Subspaces for Density-Based Outlier Ranking
In: Proc. IEEE 28th International Conference on Data Engineering (ICDE 2012)
-
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuGLSBackwardSearchAlgorithm
-
F. Chen and C.-T. Lu and A. P. Boedihardjo
GLS-SOD: A Generalized Local Statistical Approach for Spatial Outlier Detection
In: Proc. 16th ACM SIGKDD international conference on Knowledge discovery and data mining
-
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuMeanMultipleAttributes,
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuMedianMultipleAttributes
-
Chang-Tien Lu and Dechang Chen and Yufeng Kou
Detecting Spatial Outliers with Multiple Attributes
In: Proc. 15th IEEE International Conference on Tools with Artificial Intelligence, 2003
-
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuMedianAlgorithm
-
C.-T. Lu and D. Chen and Y. Kou
Algorithms for Spatial Outlier Detection
In: Proc. 3rd IEEE International Conference on Data Mining
-
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuMoranScatterplotOutlier,
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuScatterplotOutlier,
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuZTestOutlier
-
S. Shekhar and C.-T. Lu and P. Zhang
A Unified Approach to Detecting Spatial Outliers
In: GeoInformatica 7-2, 2003
-
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuRandomWalkEC
-
X. Liu and C.-T. Lu and F. Chen
Spatial outlier detection: random walk based approaches
In: Proc. 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2010
-
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.SLOM
-
Sanjay Chawla and Pei Sun
SLOM: a new measure for local spatial outliers
In: Knowledge and Information Systems 9(4), 412-429, 2006
-
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.SOF
-
Huang, T., Qin, X.
Detecting outliers in spatial database
In: Proc. 3rd International Conference on Image and Graphics
-
de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.TrimmedMeanApproach
-
Tianming Hu and Sam Yuan Sung
A trimmed mean approach to finding spatial outliers
In: Intelligent Data Analysis, Volume 8, 2004
-
de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.AbstractAggarwalYuOutlier,
de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.AggarwalYuEvolutionary,
de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.AggarwalYuNaive
-
C.C. Aggarwal, P. S. Yu
Outlier detection for high dimensional data
In: Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD 2001), Santa Barbara, CA, 2001
-
de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.OUTRES
-
E. Müller, M. Schiffer, T. Seidl
Adaptive outlierness for subspace outlier ranking
In: Proc. 19th ACM International Conference on Information and knowledge management
-
de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.OutRankS1
-
Emmanuel Müller, Ira Assent, Uwe Steinhausen, Thomas Seidl
OutRank: ranking outliers in high dimensional data
In: Proc. 24th Int. Conf. on Data Engineering (ICDE) Workshop on Ranking in Databases (DBRank), Cancun, Mexico
-
de.lmu.ifi.dbs.elki.algorithm.outlier.subspace.SOD
-
H.-P. Kriegel, P. Kröger, E. Schubert, A. Zimek
Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data
In: Proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Bangkok, Thailand, 2009
-
de.lmu.ifi.dbs.elki.algorithm.outlier.svm.LibSVMOneClassOutlierDetection
-
B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, R. C. Williamson
Estimating the support of a high-dimensional distribution
In: Neural computation 13.7
-
de.lmu.ifi.dbs.elki.algorithm.statistics.HopkinsStatisticClusteringTendency
-
B. Hopkins and J. G. Skellam
A new method for determining the type of distribution of plant individuals
In: Annals of Botany, 18(2), 213-227
-
de.lmu.ifi.dbs.elki.application.greedyensemble.ComputeKNNOutlierScores
-
E. Schubert, R. Wojdanowski, A. Zimek, H.-P. Kriegel
On Evaluation of Outlier Rankings and Outlier Scores
In: Proc. 12th SIAM International Conference on Data Mining (SDM), Anaheim, CA, 2012.
-
de.lmu.ifi.dbs.elki.application.greedyensemble.GreedyEnsembleExperiment,
de.lmu.ifi.dbs.elki.application.greedyensemble.VisualizePairwiseGainMatrix
-
E. Schubert, R. Wojdanowski, A. Zimek, H.-P. Kriegel
On Evaluation of Outlier Rankings and Outlier Scores
In: Proc. 12th SIAM International Conference on Data Mining (SDM), Anaheim, CA, 2012.
-
de.lmu.ifi.dbs.elki.data.uncertain.UnweightedDiscreteUncertainObject
-
N. Dalvi, C. Ré, D. Suciu
Probabilistic databases: diamonds in the dirt
In: Communications of the ACM 52, 7
-
de.lmu.ifi.dbs.elki.data.uncertain.WeightedDiscreteUncertainObject
-
O. Benjelloun, A. D. Sarma, A. Halevy, J. Widom
ULDBs: Databases with uncertainty and lineage
In: Proc. of the 32nd international conference on Very Large Data Bases (VLDB)
-
de.lmu.ifi.dbs.elki.database.ids.integer.IntegerDBIDArrayQuickSort,
de.lmu.ifi.dbs.elki.utilities.datastructures.arrays.IntegerArrayQuickSort
-
Vladimir Yaroslavskiy
Dual-Pivot Quicksort
In: http://iaroslavski.narod.ru/quicksort/
-
de.lmu.ifi.dbs.elki.datasource.filter.transform.LinearDiscriminantAnalysisFilter
-
R. A. Fisher
The use of multiple measurements in taxonomic problems
In: Annals of eugenics 7.2 (1936)
-
de.lmu.ifi.dbs.elki.datasource.filter.transform.PerturbationFilter
-
A. Zimek, R. J. G. B. Campello, J. Sander
Data Perturbation for Outlier Detection Ensembles
In: Proc. 26th International Conference on Scientific and Statistical Database Management (SSDBM), Aalborg, Denmark, 2014
-
de.lmu.ifi.dbs.elki.distance.distancefunction.BrayCurtisDistanceFunction
-
T. Sørensen
A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons
In: Kongelige Danske Videnskabernes Selskab 5 (4)
-
de.lmu.ifi.dbs.elki.distance.distancefunction.BrayCurtisDistanceFunction
-
J. R. Bray and J. T. Curtis
An ordination of the upland forest communities of southern Wisconsin
In: Ecological monographs 27.4
-
de.lmu.ifi.dbs.elki.distance.distancefunction.BrayCurtisDistanceFunction
-
L. R. Dice
Measures of the Amount of Ecologic Association Between Species
In: Ecology 26 (3)
-
de.lmu.ifi.dbs.elki.distance.distancefunction.CanberraDistanceFunction
-
G. N. Lance, W. T. Williams
Computer programs for hierarchical polythetic classification (similarity analyses)
In: Computer Journal, Volume 9, Issue 1
-
de.lmu.ifi.dbs.elki.distance.distancefunction.ClarkDistanceFunction,
de.lmu.ifi.dbs.elki.distance.distancefunction.Kulczynski1DistanceFunction,
de.lmu.ifi.dbs.elki.distance.distancefunction.LorentzianDistanceFunction,
de.lmu.ifi.dbs.elki.distance.similarityfunction.Kulczynski1SimilarityFunction,
de.lmu.ifi.dbs.elki.distance.similarityfunction.Kulczynski2SimilarityFunction
-
M.-M. Deza and E. Deza
Dictionary of distances
In: Dictionary of distances
-
de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram.HSBHistogramQuadraticDistanceFunction
-
J. R. Smith, S. F. Chang
VisualSEEk: a fully automated content-based image query system
In: Proceedings of the fourth ACM international conference on Multimedia 1997
-
de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram.HistogramIntersectionDistanceFunction
-
M. J. Swain, D. H. Ballard
Color Indexing
In: International Journal of Computer Vision, 7(1), 32, 1991
-
de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram.RGBHistogramQuadraticDistanceFunction
-
J. Hafner, H. S.Sawhney, W. Equits, M. Flickner, W. Niblack
Efficient Color Histogram Indexing for Quadratic Form Distance Functions
In: IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 17, No. 7, July 1995
-
de.lmu.ifi.dbs.elki.distance.distancefunction.geo.DimensionSelectingLatLngDistanceFunction,
de.lmu.ifi.dbs.elki.distance.distancefunction.geo.LatLngDistanceFunction,
de.lmu.ifi.dbs.elki.distance.distancefunction.geo.LngLatDistanceFunction,
de.lmu.ifi.dbs.elki.math.geodesy.SphereUtil
-
Erich Schubert, Arthur Zimek and Hans-Peter Kriegel
Geodetic Distance Queries on R-Trees for Indexing Geographic Data
In: Advances in Spatial and Temporal Databases - 13th International Symposium, SSTD 2013, Munich, Germany
-
de.lmu.ifi.dbs.elki.distance.distancefunction.histogram.HistogramMatchDistanceFunction
-
L.N. Vaserstein
Markov processes over denumerable products of spaces describing large systems of automata
In: Problemy Peredachi Informatsii 5.3 / Problems of Information Transmission, 5:3
-
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.ChiSquaredDistanceFunction,
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.JeffreyDivergenceDistanceFunction
-
J. Puzicha, J.M. Buhmann, Y. Rubner, C. Tomasi
Empirical evaluation of dissimilarity measures for color and texture
In: Proc. 7th IEEE International Conference on Computer Vision
-
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.HellingerDistanceFunction
-
E. Hellinger
Neue Begründung der Theorie quadratischer Formen von unendlichvielen Veränderlichen
In: Journal für die reine und angewandte Mathematik
-
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.KullbackLeiblerDivergenceAsymmetricDistanceFunction,
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction
-
S. Kullback
Information theory and statistics
In: Information theory and statistics, Courier Dover Publications, 1997.
-
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.SqrtJensenShannonDivergenceDistanceFunction
-
D. M. Endres, J. E. Schindelin
A new metric for probability distributions
In: IEEE Transactions on Information Theory, 49(7)
-
de.lmu.ifi.dbs.elki.distance.distancefunction.set.HammingDistanceFunction
-
R. W. Hamming
Error detecting and error correcting codes
In: Bell System technical journal, 29(2)
-
de.lmu.ifi.dbs.elki.distance.distancefunction.set.JaccardSimilarityDistanceFunction,
de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster.ClusterJaccardSimilarityFunction
-
P. Jaccard
Distribution de la florine alpine dans la Bassin de Dranses et dans quelques regiones voisines
In: Bulletin del la Société Vaudoise des Sciences Naturelles
-
de.lmu.ifi.dbs.elki.distance.distancefunction.strings.LevenshteinDistanceFunction,
de.lmu.ifi.dbs.elki.distance.distancefunction.strings.NormalizedLevenshteinDistanceFunction
-
V. I. Levenshtein
Binary codes capable of correcting deletions, insertions and reversals.
In: Soviet physics doklady. Vol. 10. 1966.
-
de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries.DTWDistanceFunction
-
Berndt, D. and Clifford, J.
Using dynamic time warping to find patterns in time series
In: AAAI-94 Workshop on Knowledge Discovery in Databases, 1994
-
de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries.DerivativeDTWDistanceFunction
-
E. J. Keogh and M. J. Pazzani
Derivative dynamic time warping
In: 1st SIAM International Conference on Data Mining (SDM-2001)
-
de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries.EDRDistanceFunction
-
L. Chen and M. T. Özsu and V. Oria
Robust and fast similarity search for moving object trajectories
In: SIGMOD '05: Proceedings of the 2005 ACM SIGMOD international conference on Management of data
-
de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries.ERPDistanceFunction
-
L. Chen and R. Ng
On the marriage of Lp-norms and edit distance
In: VLDB '04: Proceedings of the Thirtieth international conference on Very large data bases
-
de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries.LCSSDistanceFunction
-
M. Vlachos, M. Hadjieleftheriou, D. Gunopulos, E. Keogh
Indexing Multi-Dimensional Time-Series with Support for Multiple Distance Measures
In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
-
de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster.ClusteringAdjustedRandIndexSimilarityFunction,
de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster.ClusteringRandIndexSimilarityFunction,
de.lmu.ifi.dbs.elki.evaluation.clustering.PairCounting
-
Rand, W. M.
Objective Criteria for the Evaluation of Clustering Methods
In: Journal of the American Statistical Association, Vol. 66 Issue 336
-
de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster.ClusteringBCubedF1SimilarityFunction
-
Bagga, A. and Baldwin, B.
Entity-based cross-document coreferencing using the Vector Space Model
In: Proc. COLING '98 Proceedings of the 17th international conference on Computational linguistics
-
de.lmu.ifi.dbs.elki.distance.similarityfunction.cluster.ClusteringFowlkesMallowsSimilarityFunction,
de.lmu.ifi.dbs.elki.evaluation.clustering.PairCounting
-
Fowlkes, E.B. and Mallows, C.L.
A method for comparing two hierarchical clusterings
In: Journal of the American Statistical Association, Vol. 78 Issue 383
-
de.lmu.ifi.dbs.elki.evaluation.clustering.BCubed
-
A. Bagga and B. Baldwin
Entity-based cross-document coreferencing using the Vector Space Model
In: Proc. COLING '98 Proceedings of the 17th international conference on Computational linguistics
-
de.lmu.ifi.dbs.elki.evaluation.clustering.EditDistance
-
Pantel, P. and Lin, D.
Document clustering with committees
In: Proc. 25th ACM SIGIR conference on Research and development in information retrieval
-
de.lmu.ifi.dbs.elki.evaluation.clustering.Entropy
-
Meilă, M.
Comparing clusterings by the variation of information
In: Learning theory and kernel machines
-
de.lmu.ifi.dbs.elki.evaluation.clustering.Entropy
-
Nguyen, X. V. and Epps, J. and Bailey, J.
Information theoretic measures for clusterings comparison: is a correction for chance necessary?
In: Proc. ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
-
de.lmu.ifi.dbs.elki.evaluation.clustering.SetMatchingPurity
-
Steinbach, M. and Karypis, G. and Kumar, V.
A comparison of document clustering techniques
In: KDD workshop on text mining, 2000
-
de.lmu.ifi.dbs.elki.evaluation.clustering.SetMatchingPurity
-
E. Amigó, J. Gonzalo, J. Artiles, and F. Verdejo
A comparison of extrinsic clustering evaluation metrics based on formal constraints
In: Inf. Retrieval, vol. 12, no. 5
-
de.lmu.ifi.dbs.elki.evaluation.clustering.SetMatchingPurity
-
Meilă, M
Comparing clusterings
In: University of Washington, Seattle, Technical Report 418, 2002
-
de.lmu.ifi.dbs.elki.evaluation.clustering.SetMatchingPurity
-
Zhao, Y. and Karypis, G.
Criterion functions for document clustering: Experiments and analysis
In: University of Minnesota, Department of Computer Science, Technical Report 01-40, 2001
-
de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateCIndex
-
L. J. Hubert and J. R. Levin
A general statistical framework for assessing categorical clustering in free recall.
In: Psychological Bulletin, Vol. 83(6)
-
de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateConcordantPairs
-
F. B. Baker, and L. J. Hubert
Measuring the Power of Hierarchical Cluster Analysis
In: Journal of the American Statistical Association, 70(349)
-
de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateConcordantPairs
-
F. J. Rohlf
Methods of comparing classifications
In: Annual Review of Ecology and Systematics
-
de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateDaviesBouldin
-
D. L. Davies and D. W. Bouldin
A Cluster Separation Measure
In: IEEE Transactions Pattern Analysis and Machine Intelligence PAMI-1(2)
-
de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluatePBMIndex
-
M. K. Pakhira, and S. Bandyopadhyay, and U. Maulik
Validity index for crisp and fuzzy clusters
In: Pattern recognition, 37(3)
-
de.lmu.ifi.dbs.elki.evaluation.clustering.internal.EvaluateVarianceRatioCriteria
-
R. B. Calinski and J. Harabasz
A dendrite method for cluster analysis
In: Communications in Statistics-theory and Methods, 3(1)
-
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
-
Elke Achtert, Sascha Goldhofer, Hans-Peter Kriegel, Erich Schubert, Arthur Zimek
Evaluation of Clusterings – Metrics and Visual Support
In: Proc. 28th International Conference on Data Engineering (ICDE) 2012
-
de.lmu.ifi.dbs.elki.evaluation.outlier.OutlierSmROCCurve
-
W. Klement, P. A. Flach, N. Japkowicz, S. Matwin
Smooth Receiver Operating Characteristics (smROC) Curves
In: In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD'11)
-
de.lmu.ifi.dbs.elki.index.idistance.InMemoryIDistanceIndex
-
C. Yu, B. C. Ooi, K. L. Tan, H. V. Jagadish
Indexing the distance: An efficient method to knn processing
In: In Proceedings of the 27th International Conference on Very Large Data Bases
-
de.lmu.ifi.dbs.elki.index.idistance.InMemoryIDistanceIndex
-
H. V. Jagadish, B. C. Ooi, K. L. Tan, C. Yu, R. Zhang
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search
In: ACM Transactions on Database Systems (TODS), 30(2), 364-397
-
de.lmu.ifi.dbs.elki.index.lsh.hashfamilies.CosineHashFunctionFamily,
de.lmu.ifi.dbs.elki.index.lsh.hashfunctions.CosineLocalitySensitiveHashFunction
-
M.S. Charikar
Similarity estimation techniques from rounding algorithms
In: Proc. 34th ACM Symposium on Theory of computing, STOC'02
-
de.lmu.ifi.dbs.elki.index.lsh.hashfamilies.EuclideanHashFunctionFamily,
de.lmu.ifi.dbs.elki.index.lsh.hashfamilies.ManhattanHashFunctionFamily,
de.lmu.ifi.dbs.elki.index.lsh.hashfunctions.MultipleProjectionsLocalitySensitiveHashFunction
-
M. Datar and N. Immorlica and P. Indyk and V. S. Mirrokni
Locality-sensitive hashing scheme based on p-stable distributions
In: Proc. 20th annual symposium on Computational geometry
-
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
-
E. Schubert, A. Zimek, H.-P. Kriegel
Fast and Scalable Outlier Detection with Approximate Nearest Neighbor Ensembles
In: Proc. 20th International Conference on Database Systems for Advanced Applications (DASFAA)
-
de.lmu.ifi.dbs.elki.index.preprocessed.knn.RandomSampleKNNPreprocessor
-
A. Zimek and M. Gaudet and R. J. G. B. Campello and J. Sander
Subsampling for Efficient and Effective Unsupervised Outlier Detection Ensembles
In: Proc. 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '13
-
de.lmu.ifi.dbs.elki.index.projected.PINN
-
T. de Vries, S. Chawla, M. E. Houle
Finding local anomalies in very high dimensional space
In: Proc. IEEE 10th International Conference on Data Mining (ICDM)
-
de.lmu.ifi.dbs.elki.index.tree.metrical.covertree.CoverTree
-
A. Beygelzimer, S. Kakade, J. Langford
Cover trees for nearest neighbor
In: In Proc. 23rd International Conference on Machine Learning (ICML)
-
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree.MTree,
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.insert.MinimumEnlargementInsert,
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
-
P. Ciaccia, M. Patella, P. Zezula
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
In: VLDB'97, Proceedings of 23rd International Conference on Very Large Data Bases, August 25-29, 1997, Athens, Greece
-
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
-
J. L. Bentley
Multidimensional binary search trees used for associative searching
In: Communications of the ACM, Vol. 18 Issue 9, Sept. 1975
-
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query.EuclideanRStarTreeKNNQuery,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query.RStarTreeKNNQuery
-
G. R. Hjaltason, H. Samet
Ranking in spatial databases
In: Advances in Spatial Databases - 4th Symposium, SSD'95
-
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query.EuclideanRStarTreeRangeQuery,
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query.RStarTreeRangeQuery
-
J. Kuan, P. Lewis
Fast k nearest neighbour search for R-tree family
In: Proc. Int. Conf Information, Communications and Signal Processing, ICICS 1997
-
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
-
N. Beckmann, H.-P. Kriegel, R. Schneider, B. Seeger
The R*-tree: an efficient and robust access method for points and rectangles
In: Proceedings of the 1990 ACM SIGMOD International Conference on Management of Data, Atlantic City, NJ, May 23-25, 1990
-
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.bulk.OneDimSortBulkSplit
-
Roussopoulos, N. and Leifker, D.
Direct spatial search on pictorial databases using packed R-trees
In: ACM SIGMOD Record 14-4
-
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.bulk.SortTileRecursiveBulkSplit
-
Leutenegger, S.T. and Lopez, M.A. and Edgington, J.
STR: A simple and efficient algorithm for R-tree packing
In: Proc. 13th International Conference on Data Engineering, 1997
-
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.bulk.SpatialSortBulkSplit
-
Kamel, I. and Faloutsos, C.
On packing R-trees
In: Proc. of the second international conference on Information and knowledge management
-
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
-
Antonin Guttman
R-Trees: A Dynamic Index Structure For Spatial Searching
In: Proceedings of the 1984 ACM SIGMOD international conference on Management of data
-
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.split.AngTanLinearSplit
-
C. H. Ang and T. C. Tan
New linear node splitting algorithm for R-trees
In: Proceedings of the 5th International Symposium on Advances in Spatial Databases
-
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.split.GreeneSplit
-
Diane Greene
An implementation and performance analysis of spatial data access methods
In: Proceedings of the Fifth International Conference on Data Engineering
-
de.lmu.ifi.dbs.elki.index.vafile.DAFile,
de.lmu.ifi.dbs.elki.index.vafile.PartialVAFile
-
Hans-Peter Kriegel, Peer Kröger, Matthias Schubert, Ziyue Zhu
Efficient Query Processing in Arbitrary Subspaces Using Vector Approximations
In: Proc. 18th Int. Conf. on Scientific and Statistical Database Management (SSDBM 06), Wien, Austria, 2006
-
de.lmu.ifi.dbs.elki.index.vafile.VAFile
-
Weber, R. and Blott, S.
An approximation based data structure for similarity search
In: Report TR1997b, ETH Zentrum, Zurich, Switzerland
-
de.lmu.ifi.dbs.elki.math.Mean,
de.lmu.ifi.dbs.elki.math.MeanVariance
-
B. P. Welford
Note on a method for calculating corrected sums of squares and products
In: Technometrics 4(3)
-
de.lmu.ifi.dbs.elki.math.MeanVariance
-
D.H.D. West
Updating Mean and Variance Estimates: An Improved Method
In: Communications of the ACM, Volume 22 Issue 9
-
de.lmu.ifi.dbs.elki.math.StatisticalMoments
-
T. B. Terriberry
Computing Higher-Order Moments Online
In: Online - Technical Note
-
de.lmu.ifi.dbs.elki.math.dimensionsimilarity.HSMDimensionSimilarity,
de.lmu.ifi.dbs.elki.math.statistics.dependence.HSMDependenceMeasure
-
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
In: IEEE Trans. Visualization and Computer Graphics, 2011
-
de.lmu.ifi.dbs.elki.math.dimensionsimilarity.HiCSDimensionSimilarity,
de.lmu.ifi.dbs.elki.math.dimensionsimilarity.SURFINGDimensionSimilarity,
de.lmu.ifi.dbs.elki.math.dimensionsimilarity.SlopeDimensionSimilarity,
de.lmu.ifi.dbs.elki.math.dimensionsimilarity.SlopeInversionDimensionSimilarity,
de.lmu.ifi.dbs.elki.math.statistics.dependence.HiCSDependenceMeasure,
de.lmu.ifi.dbs.elki.math.statistics.dependence.SURFINGDependenceMeasure,
de.lmu.ifi.dbs.elki.math.statistics.dependence.SlopeDependenceMeasure,
de.lmu.ifi.dbs.elki.math.statistics.dependence.SlopeInversionDependenceMeasure
-
Elke Achtert, Hans-Peter Kriegel, Erich Schubert, Arthur Zimek
Interactive Data Mining with 3D-Parallel-Coordinate-Trees
In: Proc. of the 2013 ACM International Conference on Management of Data (SIGMOD)
-
de.lmu.ifi.dbs.elki.math.dimensionsimilarity.MCEDimensionSimilarity,
de.lmu.ifi.dbs.elki.math.statistics.dependence.MCEDependenceMeasure
-
D. Guo
Coordinating computational and visual approaches for interactive feature selection and multivariate clustering
In: Information Visualization, 2(4)
-
de.lmu.ifi.dbs.elki.math.dimensionsimilarity.SURFINGDimensionSimilarity,
de.lmu.ifi.dbs.elki.math.statistics.dependence.SURFINGDependenceMeasure
-
Christian Baumgartner, Claudia Plant, Karin Kailing, Hans-Peter Kriegel, and Peer Kröger
Subspace Selection for Clustering High-Dimensional Data
In: IEEE International Conference on Data Mining, 2004
-
de.lmu.ifi.dbs.elki.math.geodesy.SphereUtil
-
Ed Williams
Aviation Formulary
-
de.lmu.ifi.dbs.elki.math.geodesy.SphereUtil
-
T. Vincenty
Direct and inverse solutions of geodesics on the ellipsoid with application of nested equations
In: Survey review 23 176, 1975
-
de.lmu.ifi.dbs.elki.math.geodesy.SphereUtil
-
Sinnott, R. W.
Virtues of the Haversine
In: Sky and telescope, 68-2, 1984
-
de.lmu.ifi.dbs.elki.math.geometry.GrahamScanConvexHull2D
-
Paul Graham
An Efficient Algorithm for Determining the Convex Hull of a Finite Planar Set
In: Information Processing Letters 1
-
de.lmu.ifi.dbs.elki.math.geometry.PrimsMinimumSpanningTree
-
R. C. Prim
Shortest connection networks and some generalizations
In: Bell System Technical Journal, 36 (1957)
-
de.lmu.ifi.dbs.elki.math.geometry.SweepHullDelaunay2D
-
David Sinclair
S-hull: a fast sweep-hull routine for Delaunay triangulation
Online: http://s-hull.org/
-
de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCAFilteredAutotuningRunner,
de.lmu.ifi.dbs.elki.math.linearalgebra.pca.WeightedCovarianceMatrixBuilder
-
Hans-Peter Kriegel, Peer Kröger, Erich Schubert, Arthur Zimek
A General Framework for Increasing the Robustness of PCA-based Correlation Clustering Algorithms
In: Proceedings of the 20th International Conference on Scientific and Statistical Database Management (SSDBM), Hong Kong, China, 2008
-
de.lmu.ifi.dbs.elki.math.linearalgebra.pca.RANSACCovarianceMatrixBuilder,
de.lmu.ifi.dbs.elki.utilities.scaling.outlier.COPOutlierScaling
-
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)
-
de.lmu.ifi.dbs.elki.math.linearalgebra.pca.RANSACCovarianceMatrixBuilder
-
M.A. Fischler, R.C. Bolles
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
In: Communications of the ACM, Vol. 24 Issue 6
-
de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections.AchlioptasRandomProjectionFamily
-
D. Achlioptas
Database-friendly random projections: Johnson-Lindenstrauss with binary coins
In: Proc. 20th ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
-
de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections.CauchyRandomProjectionFamily,
de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections.GaussianRandomProjectionFamily
-
M. Datar and N. Immorlica and P. Indyk and V. S. Mirrokni
Locality-sensitive hashing scheme based on p-stable distributions
In: Proc. 20th Symposium on Computational Geometry
-
de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections.RandomSubsetProjectionFamily
-
L. Breiman
Bagging predictors
In: Machine learning 24.2
-
de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections.SimplifiedRandomHyperplaneProjectionFamily
-
M. Henzinger
Finding near-duplicate web pages: a large-scale evaluation of algorithms
In: Proc. 29th ACM Conference on Research and Development in Information Retrieval. ACM SIGIR, 2006
-
de.lmu.ifi.dbs.elki.math.random.XorShift1024NonThreadsafeRandom,
de.lmu.ifi.dbs.elki.math.random.XorShift64NonThreadsafeRandom
-
S. Vigna
An experimental exploration of Marsaglia's xorshift generators, scrambled
-
de.lmu.ifi.dbs.elki.math.spacefillingcurves.HilbertSpatialSorter
-
D. Hilbert
Über die stetige Abbildung einer Linie auf ein Flächenstück
In: Mathematische Annalen, 38(3)
-
de.lmu.ifi.dbs.elki.math.spacefillingcurves.PeanoSpatialSorter
-
G. Peano
Sur une courbe, qui remplit toute une aire plane
In: Mathematische Annalen, 36(1)
-
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
-
J.R.M. Hosking, J. R. Wallis, and E. F. Wood
Estimation of the generalized extreme-value distribution by the method of probability-weighted moments.
In: Technometrics 27.3
-
de.lmu.ifi.dbs.elki.math.statistics.dependence.DistanceCorrelationDependenceMeasure
-
Székely, G. J., Rizzo, M. L., & Bakirov, N. K.
Measuring and testing dependence by correlation of distances
In: The Annals of Statistics, 35(6), 2769-2794
-
de.lmu.ifi.dbs.elki.math.statistics.dependence.HoeffdingsDDependenceMeasure
-
W. Hoeffding
A non-parametric test of independence
In: The Annals of Mathematical Statistics 19
-
de.lmu.ifi.dbs.elki.math.statistics.distribution.ChiSquaredDistribution,
de.lmu.ifi.dbs.elki.math.statistics.distribution.GammaDistribution
-
D.J. Best, D. E. Roberts
Algorithm AS 91: The percentage points of the $\chi^2$ distribution
In: Journal of the Royal Statistical Society. Series C (Applied Statistics)
-
de.lmu.ifi.dbs.elki.math.statistics.distribution.GammaDistribution
-
J. M. Bernando
Algorithm AS 103: Psi (Digamma) Function
In: Statistical Algorithms
-
de.lmu.ifi.dbs.elki.math.statistics.distribution.HaltonUniformDistribution
-
Wang, X. and Hickernell, F.J.
Randomized halton sequences
In: Mathematical and Computer Modelling Vol. 32 (7)
-
de.lmu.ifi.dbs.elki.math.statistics.distribution.PoissonDistribution
-
C. Loader
Fast and accurate computation of binomial probabilities
-
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.CauchyMADEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.ExponentialMADEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.GumbelMADEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LaplaceMADEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LogLogisticMADEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.RayleighMADEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.UniformMADEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.WeibullLogMADEstimator
-
D. J. Olive
Applied Robust Statistics
In: Applied Robust Statistics
-
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.EMGOlivierNorbergEstimator
-
J. Olivier, M. M. Norberg
Positively skewed data: Revisiting the Box-Cox power transformation
In: International Journal of Psychological Research Vol. 3 No. 1
-
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
-
J.R.M. Hosking
Fortran routines for use with the method of L-moments Version 3.03
In: IBM Research Technical Report
-
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.ExponentialMedianEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LogisticMADEstimator
-
D. J. Olive
Robust Estimators for Transformed Location Scale Families
-
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.GammaChoiWetteEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LogGammaChoiWetteEstimator
-
S. C. Choi, R. Wette
Maximum likelihood estimation of the parameters of the gamma distribution and their bias
In: Technometrics
-
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.GammaMADEstimator
-
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
-
G. Casella, R. L. Berger
Statistical inference. Vol. 70
In: Statistical inference. Vol. 70
-
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LaplaceMLEEstimator
-
R. M. Norton
The Double Exponential Distribution: Using Calculus to Find a Maximum Likelihood Estimator
In: The American Statistician 38 (2)
-
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LogNormalBilkovaLMMEstimator
-
D. Bílková
Lognormal distribution and using L-moment method for estimating its parameters
In: Int. Journal of Mathematical Models and Methods in Applied Sciences (NAUN)
-
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LogNormalLogMADEstimator,
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.NormalMADEstimator
-
F. R. Hampel
The Influence Curve and Its Role in Robust Estimation
In: Journal of the American Statistical Association, June 1974, Vol. 69, No. 346
-
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.meta.WinsorisingEstimator
-
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)
-
de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.AggregatedHillEstimator
-
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
-
de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.GEDEstimator
-
M. E. Houle, H. Kashima, M. Nett
Generalized expansion dimension
In: 12th International Conference on Data Mining Workshops (ICDMW)
-
de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.HillEstimator
-
B. M. Hill
A simple general approach to inference about the tail of a distribution
In: The annals of statistics 3(5)
-
de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.MOMEstimator,
de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.RVEstimator
-
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
-
de.lmu.ifi.dbs.elki.math.statistics.intrinsicdimensionality.ZipfEstimator
-
M. Kratz and S. I. Resnick
On Least Squares Estimates of an Exponential Tail Coefficient
In: Statistics & Risk Modeling. Band 14, Heft 4
-
de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.BiweightKernelDensityFunction,
de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.EpanechnikovKernelDensityFunction,
de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.GaussianKernelDensityFunction,
de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.KernelDensityFunction,
de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.TriweightKernelDensityFunction,
de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.UniformKernelDensityFunction
-
J.S. Marron, D. Nolan
Canonical kernels for density estimation
In: Statistics & Probability Letters, Volume 7, Issue 3
-
de.lmu.ifi.dbs.elki.math.statistics.tests.AndersonDarlingTest
-
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)
-
de.lmu.ifi.dbs.elki.math.statistics.tests.AndersonDarlingTest
-
M. A. Stephens
EDF Statistics for Goodness of Fit and Some Comparisons
In: Journal of the American Statistical Association, Volume 69, Issue 347
-
de.lmu.ifi.dbs.elki.math.statistics.tests.StandardizedTwoSampleAndersonDarlingTest
-
A. N. Pettitt
A two-sample Anderson-Darling rank statistic
In: Biometrika 63 (1)
-
de.lmu.ifi.dbs.elki.math.statistics.tests.StandardizedTwoSampleAndersonDarlingTest
-
F. W. Scholz, and M. A. Stephens
K-sample Anderson–Darling tests
In: Journal of the American Statistical Association, 82(399)
-
de.lmu.ifi.dbs.elki.math.statistics.tests.StandardizedTwoSampleAndersonDarlingTest
-
D. A. Darling
The Kolmogorov-Smirnov, Cramer-von Mises tests
In: Annals of mathematical statistics 28(4)
-
de.lmu.ifi.dbs.elki.result.KMLOutputHandler
-
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
-
de.lmu.ifi.dbs.elki.utilities.datastructures.unionfind.WeightedQuickUnionInteger,
de.lmu.ifi.dbs.elki.utilities.datastructures.unionfind.WeightedQuickUnionRangeDBIDs,
de.lmu.ifi.dbs.elki.utilities.datastructures.unionfind.WeightedQuickUnionStaticDBIDs
-
R. Sedgewick
1.3 Union-Find Algorithms
In: Algorithms in C, Parts 1-4
-
de.lmu.ifi.dbs.elki.utilities.scaling.outlier.COPOutlierScaling
-
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
-
de.lmu.ifi.dbs.elki.utilities.scaling.outlier.HeDESNormalizationOutlierScaling
-
H. V. Nguyen, H. H. Ang, V. Gopalkrishnan
Mining Outliers with Ensemble of Heterogeneous Detectors on Random Subspaces
In: Proc. 15th International Conference on Database Systems for Advanced Applications (DASFAA 2010)
-
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
-
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
-
de.lmu.ifi.dbs.elki.utilities.scaling.outlier.MixtureModelOutlierScalingFunction,
de.lmu.ifi.dbs.elki.utilities.scaling.outlier.SigmoidOutlierScalingFunction
-
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.
-
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.density.DensityEstimationOverlay$Instance
-
D. W. Scott
Multivariate density estimation: Theory, Practice, and Visualization
In: Multivariate Density Estimation: Theory, Practice, and Visualization
-
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.outlier.BubbleVisualization
-
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