ELKI references overview:

de.lmu.ifi.dbs.elki.algorithm.APRIORI
R. Agrawal, R. Srikant
Fast Algorithms for Mining Association Rules in Large Databases
In: Proc. 20th Int. Conf. on Very Large Data Bases (VLDB '94), Santiago de Chile, Chile 1994
http://www.acm.org/sigmod/vldb/conf/1994/P487.PDF
de.lmu.ifi.dbs.elki.algorithm.DependencyDerivator
E. Achtert, C. Böhm, H.-P. Kriegel, P. Kröger, A. Zimek
Deriving Quantitative Dependencies for Correlation Clusters
In: Proc. 12th Int. Conf. on Knowledge Discovery and Data Mining (KDD '06), Philadelphia, PA 2006.
http://dx.doi.org/10.1145/1150402.1150408
de.lmu.ifi.dbs.elki.algorithm.clustering.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, and X. Xu
A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise
In: Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining (KDD '96), Portland, OR, 1996
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.71.1980
de.lmu.ifi.dbs.elki.algorithm.clustering.DeLiClu
E. Achtert, C. Böhm, P. Kröger
DeLiClu: Boosting Robustness, Completeness, Usability, and Efficiency of Hierarchical Clustering by a Closest Pair Ranking
In: Proc. 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006), Singapore, 2006
http://dx.doi.org/10.1007/11731139_16
de.lmu.ifi.dbs.elki.algorithm.clustering.EM
A. P. Dempster, N. M. Laird, D. B. Rubin
Maximum Likelihood from Incomplete Data via the EM algorithm
In: Journal of the Royal Statistical Society, Series B, 39(1), 1977, pp. 1-31
http://www.jstor.org/stable/2984875
de.lmu.ifi.dbs.elki.algorithm.clustering.OPTICS
M. Ankerst, M. Breunig, H.-P. Kriegel, and J. Sander
OPTICS: Ordering Points to Identify the Clustering Structure
In: Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD '99)
http://dx.doi.org/10.1145/304181.304187
de.lmu.ifi.dbs.elki.algorithm.clustering.SLINK
R. Sibson
SLINK: An optimally efficient algorithm for the single-link cluster method
In: The Computer Journal 16 (1973), No. 1, p. 30-34.
http://dx.doi.org/10.1093/comjnl/16.1.30
de.lmu.ifi.dbs.elki.algorithm.clustering.SNNClustering
L. Ertöz, M. Steinbach, V. Kumar
Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data
In: Proc. of SIAM Data Mining (SDM), 2003
http://www.siam.org/meetings/sdm03/proceedings/sdm03_05.pdf
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.CASH
E. Achtert, C. Böhm, J. David, P. Kröger, A. Zimek
Robust clustering in arbitraily oriented subspaces
In: Proc. 8th SIAM Int. Conf. on Data Mining (SDM'08), Atlanta, GA, 2008
http://www.siam.org/proceedings/datamining/2008/dm08_69_AchtertBoehmDavidKroegerZimek.pdf
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.COPAC
E. Achtert, C. Böhm, H.-P. Kriegel, P. Kröger P., A. Zimek
Robust, Complete, and Efficient Correlation Clustering
In: Proc. 7th SIAM International Conference on Data Mining (SDM'07), Minneapolis, MN, 2007
http://www.siam.org/proceedings/datamining/2007/dm07_037achtert.pdf
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.ERiC
E. Achtert, C. Böhm, H.-P. Kriegel, P. Kröger, and A. Zimek
On Exploring Complex Relationships of Correlation Clusters
In: Proc. 19th International Conference on Scientific and Statistical Database Management (SSDBM 2007), Banff, Canada, 2007
http://dx.doi.org/10.1109/SSDBM.2007.21
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.FourC
C. Böhm, K. Kailing, P. Kröger, A. Zimek
Computing Clusters of Correlation Connected Objects
In: Proc. ACM SIGMOD Int. Conf. on Management of Data, Paris, France, 2004, 455-466
http://dx.doi.org/10.1145/1007568.1007620
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.HiCO
E. Achtert, C. Böhm, P. Kröger, A. Zimek
Mining Hierarchies of Correlation Clusterse
In: Proc. Int. Conf. on Scientific and Statistical Database Management (SSDBM'06), Vienna, Austria, 2006
http://dx.doi.org/10.1109/SSDBM.2006.35
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.LMCLUS
Robert Haralick, Rave Harpaz
Linear manifold clustering in high dimensional spaces by stochastic search
In: Pattern Recognition volume 40, Issue 10
http://dx.doi.org/10.1016/j.patcog.2007.01.020
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.ORCLUS
C. C. Aggarwal, P. S. Yu
Finding Generalized Projected Clusters in High Dimensional Spaces
In: Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD '00)
http://dx.doi.org/10.1145/342009.335383
de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.GeneralizedDBSCAN
Jörg Sander, Martin Ester, Hans-Peter Kriegel, Xiaowei Xu
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
In: Data Mining and Knowledge Discovery
http://dx.doi.org/10.1023/A:1009745219419
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansLloyd
S. Lloyd
Least squares quantization in PCM
In: IEEE Transactions on Information Theory 28 (2): 129–137.
http://dx.doi.org/10.1109/TIT.1982.1056489
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansMacQueen
J. MacQueen
Some Methods for Classification and Analysis of Multivariate Observations
In: 5th Berkeley Symp. Math. Statist. Prob., Vol. 1, 1967, pp 281-297
http://projecteuclid.org/euclid.bsmsp/1200512992
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansPlusPlusInitialMeans
D. Arthur, S. Vassilvitskii
k-means++: the advantages of careful seeding
In: Proc. of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2007
http://dx.doi.org/10.1145/1283383.1283494
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMediansLloyd
P. S. Bradley, O. L. Mangasarian, W. N. Street
Clustering via Concave Minimization
In: Advances in neural information processing systems
http://nips.djvuzone.org/djvu/nips09/0368.djvu
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMedoidsPAM, de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.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.subspace.CLIQUE
R. Agrawal, J. Gehrke, D. Gunopulos, P. Raghavan
Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications
In: Proc. SIGMOD Conference, Seattle, WA, 1998
http://dx.doi.org/10.1145/276304.276314
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.DiSH
E. Achtert, C. Böhm, H.-P. Kriegel, P. Kröger, I. Müller-Gorman, A. Zimek
Detection and Visualization of Subspace Cluster Hierarchies
In: Proc. 12th International Conference on Database Systems for Advanced Applications (DASFAA), Bangkok, Thailand, 2007
http://dx.doi.org/10.1007/978-3-540-71703-4_15
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.HiSC
E. Achtert, C. Böhm, H.-P. Kriegel, P. Kröger, I. Müller-Gorman, A. Zimek
Finding Hierarchies of Subspace Clusters
In: Proc. 10th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'06), Berlin, Germany, 2006
http://www.dbs.ifi.lmu.de/Publikationen/Papers/PKDD06-HiSC.pdf
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.PROCLUS
C. C. Aggarwal, C. Procopiuc, J. L. Wolf, P. S. Yu, J. S. Park
Fast Algorithms for Projected Clustering
In: Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD '99)
http://dx.doi.org/10.1145/304181.304188
de.lmu.ifi.dbs.elki.algorithm.clustering.subspace.PreDeCon
C. Böhm, K. Kailing, H.-P. Kriegel, P. Kröger
Density Connected Clustering with Local Subspace Preferences
In: Proc. 4th IEEE Int. Conf. on Data Mining (ICDM'04), Brighton, UK, 2004
http://dx.doi.org/10.1109/ICDM.2004.10087
de.lmu.ifi.dbs.elki.algorithm.clustering.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.outlier.ABOD
H.-P. Kriegel, M. Schubert, and A. Zimek
Angle-Based Outlier Detection in High-dimensional Data
In: Proc. 14th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD '08), Las Vegas, NV, 2008
http://dx.doi.org/10.1145/1401890.1401946
de.lmu.ifi.dbs.elki.algorithm.outlier.ALOCI, de.lmu.ifi.dbs.elki.algorithm.outlier.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), Bangalore, India, 2003
http://dx.doi.org/10.1109/ICDE.2003.1260802
de.lmu.ifi.dbs.elki.algorithm.outlier.AbstractAggarwalYuOutlier, de.lmu.ifi.dbs.elki.algorithm.outlier.AggarwalYuEvolutionary, de.lmu.ifi.dbs.elki.algorithm.outlier.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
http://dx.doi.org/10.1145/375663.375668
de.lmu.ifi.dbs.elki.algorithm.outlier.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.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.GaussianUniformMixture
Generalization using the likelihood gain as outlier score of
Eskin, Eleazar
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.HilOut
F. Angiulli, C. Pizzuti
Fast Outlier Detection in High Dimensional Spaces
In: Proc. European Conference on Principles of Knowledge Discovery and Data Mining (PKDD'02)
http://dx.doi.org/10.1145/375663.375668
de.lmu.ifi.dbs.elki.algorithm.outlier.INFLO
Jin, W., Tung, A., Han, J., and Wang, W
Ranking outliers using symmetric neighborhood relationship
In: Proc. Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD), Singapore, 2006
http://dx.doi.org/10.1007/11731139_68
de.lmu.ifi.dbs.elki.algorithm.outlier.KNNOutlier
S. Ramaswamy, R. Rastogi, K. Shim
Efficient Algorithms for Mining Outliers from Large Data Sets
In: Proc. of the Int. Conf. on Management of Data, Dallas, Texas, 2000
http://dx.doi.org/10.1145/342009.335437
de.lmu.ifi.dbs.elki.algorithm.outlier.KNNWeightOutlier
F. Angiulli, C. Pizzuti
Fast Outlier Detection in High Dimensional Spaces
In: Proc. European Conference on Principles of Knowledge Discovery and Data Mining (PKDD'02), Helsinki, Finland, 2002
http://dx.doi.org/10.1007/3-540-45681-3_2
de.lmu.ifi.dbs.elki.algorithm.outlier.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), Bangkok, Thailand, 2009
http://dx.doi.org/10.1007/978-3-642-01307-2_84
de.lmu.ifi.dbs.elki.algorithm.outlier.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
http://dx.doi.org/10.1145/342009.335388
de.lmu.ifi.dbs.elki.algorithm.outlier.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
http://dx.doi.org/10.1145/1645953.1646195
de.lmu.ifi.dbs.elki.algorithm.outlier.OPTICSOF
M. M. Breunig, H.-P. Kriegel, R. Ng, and J. Sander
OPTICS-OF: Identifying Local Outliers
In: Proc. of the 3rd European Conference on Principles of Knowledge Discovery and Data Mining (PKDD), Prague, Czech Republic
http://springerlink.metapress.com/content/76bx6413gqb4tvta/
de.lmu.ifi.dbs.elki.algorithm.outlier.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), Hong Kong, China, 2006
http://dx.doi.org/10.1109/ICDM.2006.17
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
http://dx.doi.org/10.1145/1081870.1081891
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
http://dx.doi.org/10.1145/1835804.1835939
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
http://dx.doi.org/10.1109/TAI.2003.1250179
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
http://dx.doi.org/10.1109/ICDM.2003.1250986
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
http://dx.doi.org/10.1023/A:1023455925009
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
http://dx.doi.org/10.1145/1869790.1869841
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
http://dx.doi.org/10.1007/s10115-005-0200-2
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
http://dx.doi.org/10.1109/ICIG.2004.53
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
http://iospress.metapress.com/content/PLVLT6431DVNJXNK
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
http://dx.doi.org/10.1109/ICDEW.2008.4498387
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
http://dx.doi.org/10.1007/978-3-642-01307-2
de.lmu.ifi.dbs.elki.application.greedyensemble.ComputeKNNOutlierScores, 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.application.visualization.KNNExplorer
E. Achtert, T. Bernecker, H.-P. Kriegel, E. Schubert, A. Zimek
ELKI in Time: ELKI 0.2 for the Performance Evaluation of Distance Measures for Time Series
In: Proceedings of the 11th International Symposium on Spatial and Temporal Databases (SSTD), Aalborg, Denmark, 2009
http://dx.doi.org/10.1007/978-3-642-02982-0_35
de.lmu.ifi.dbs.elki.distance.distancefunction.CanberraDistanceFunction
G. N. Lance, W. T. Williams
Computer programs for hierarchical polythetic classification (similarity analysis).
In: Computer Journal, Volume 9, Issue 1
http://comjnl.oxfordjournals.org/content/9/1/60.short
de.lmu.ifi.dbs.elki.distance.distancefunction.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
http://dx.doi.org/10.1109/ICCV.1999.790412
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
http://dx.doi.org/10.1145/244130.244151
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
http://dx.doi.org/10.1109/34.391417
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
http://www.aaai.org/Papers/Workshops/1994/WS-94-03/WS94-03-031.pdf
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
http://dx.doi.org/10.1145/1066157.1066213
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
http://www.vldb.org/conf/2004/RS21P2.PDF
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
http://dx.doi.org/10.1145/956750.956777
de.lmu.ifi.dbs.elki.evaluation.clustering.BCubed
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
http://dx.doi.org/10.3115/980451.980859
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
http://dx.doi.org/10.1145/564376.564412
de.lmu.ifi.dbs.elki.evaluation.clustering.Entropy
Meilă, M.
Comparing clusterings by the variation of information
In: Learning theory and kernel machines Volume 2777/2003
http://dx.doi.org/10.1007/978-3-540-45167-9_14
de.lmu.ifi.dbs.elki.evaluation.clustering.Entropy
Vinh, N.X. 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
http://dx.doi.org/10.1145/1553374.1553511
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.PairCounting
Rand, W. M.
Objective Criteria for the Evaluation of Clustering Methods
In: Journal of the American Statistical Association, Vol. 66 Issue 336
http://www.jstor.org/stable/10.2307/2284239
de.lmu.ifi.dbs.elki.evaluation.clustering.SetMatchingPurity
Meilă, M
Comparing clusterings
In: University of Washington, Seattle, Technical Report 418, 2002
http://www.stat.washington.edu/mmp/www.stat.washington.edu/mmp/Papers/compare-colt.pdf
de.lmu.ifi.dbs.elki.evaluation.clustering.SetMatchingPurity
Steinbach, M. and Karypis, G. and Kumar, V. and others
A comparison of document clustering techniques
In: KDD workshop on text mining, 2000
http://www-users.itlabs.umn.edu/~karypis/publications/Papers/PDF/doccluster.pdf
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
http://www-users.cs.umn.edu/~karypis/publications/Papers/PDF/vscluster.pdf
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
http://elki.dbs.ifi.lmu.de/wiki/PairSegments
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)
http://dx.doi.org/10.1007/978-3-642-23783-6_13
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree.MTree
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
http://www.vldb.org/conf/1997/P426.PDF
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query.DoubleDistanceRStarTreeKNNQuery, de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query.GenericRStarTreeKNNQuery
G. R. Hjaltason, H. Samet
Ranking in spatial databases
In: Advances in Spatial Databases - 4th Symposium, SSD'95
http://dx.doi.org/10.1007/3-540-60159-7_6
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query.DoubleDistanceRStarTreeRangeQuery, de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.query.GenericRStarTreeRangeQuery
J. Kuan, P. Lewis
Fast k nearest neighbour search for R-tree family
In: Proc. Int. Conf Information, Communications and Signal Processing, ICICS 1997
http://dx.doi.org/10.1109/ICICS.1997.652114
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
http://dx.doi.org/10.1145/93597.98741
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
http://dx.doi.org/10.1145/971699.318900
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
http://dx.doi.org/10.1109/ICDE.1997.582015
de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.bulk.SpatialSortBulkSplit
Kamel, I. and Faloutsos, C.
On packing R-trees
In: Proc. 2of the second international conference on Information and knowledge management
http://dx.doi.org/10.1145/170088.170403
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
http://dx.doi.org/10.1145/971697.602266
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
http://dx.doi.org/10.1007/3-540-63238-7_38
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
http://dx.doi.org/10.1109/ICDE.1989.47268
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
http://dx.doi.org/10.1109/SSDBM.2006.23
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
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.40.480&rep=rep1&type=pdf
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.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.SweepHullDelaunay2D
David Sinclair
S-hull: a fast sweep-hull routine for Delaunay triangulation
In: Online: http://s-hull.org/
de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCAFilteredAutotuningRunner, de.lmu.ifi.dbs.elki.math.linearalgebra.pca.WeightedCovarianceMatrixBuilder
H.-P. Kriegel, P. Kröger, E. Schubert, A. 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
http://dx.doi.org/10.1007/978-3-540-69497-7_27
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
http://dx.doi.org/10.1145/361002.361007
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.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.GammaDistribution
S. C. Choi, R. Wette
Maximum likelihood estimation of the parameters of the gamma distribution and their bias
In: Technometrics
http://www.jstor.org/stable/10.2307/1266892
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.PoissonDistribution, de.lmu.ifi.dbs.elki.math.statistics.distribution.PoissonDistribution, de.lmu.ifi.dbs.elki.math.statistics.distribution.PoissonDistribution, de.lmu.ifi.dbs.elki.math.statistics.distribution.PoissonDistribution, de.lmu.ifi.dbs.elki.math.statistics.distribution.PoissonDistribution
C. Loader
Fast and accurate computation of binomial probabilities
In:
http://projects.scipy.org/scipy/raw-attachment/ticket/620/loader2000Fast.pdf
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.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)
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
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
http://siam.omnibooksonline.com/2011datamining/data/papers/018.pdf
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.
http://dx.doi.org/10.1109/ICDM.2006.43
de.lmu.ifi.dbs.elki.visualization.visualizers.scatterplot.density.DensityEstimationOverlay
D. W. Scott
Multivariate density estimation
In: Multivariate Density Estimation: Theory, Practice, and Visualization
http://dx.doi.org/10.1002/9780470316849.fmatter
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
http://dx.doi.org/10.1007/978-3-642-12098-5_34