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.NaiveMeanShiftClustering: Y. Cheng

Mean shift, mode seeking, and clustering

In: IEEE Transactions on Pattern Analysis and Machine Intelligence 17-8

http://dx.doi.org/10.1109/34.400568
de.lmu.ifi.dbs.elki.algorithm.clustering.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.COP, de.lmu.ifi.dbs.elki.math.linearalgebra.pca.RANSACCovarianceMatrixBuilder, 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.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.LDF: L. J. Latecki, A. Lazarevic, D. Pokrajac

Outlier Detection with Kernel Density Functions

In: Machine Learning and Data Mining in Pattern Recognition

http://dx.doi.org/10.1007/978-3-540-73499-4_6
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.SimpleCOP: Arthur Zimek

Correlation Clustering. PhD thesis, Chapter 18

In:
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)

http://dx.doi.org/10.1109/ICDE.2012.88
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.database.ids.integer.IntegerDBIDArrayQuickSort, de.lmu.ifi.dbs.elki.utilities.datastructures.arrays.IntegerArrayQuickSort: Vladimir Yaroslavskiy

Dual-Pivot Quicksort

In: http://iaroslavski.narod.ru/quicksort/

http://iaroslavski.narod.ru/quicksort/
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://dx.doi.org/10.1109/ICDE.2012.128
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.GeoUtil: Ed Williams

Aviation Formulary

In:

http://williams.best.vwh.net/avform.htm
de.lmu.ifi.dbs.elki.math.GeoUtil, de.lmu.ifi.dbs.elki.math.GeoUtil: Sinnott, R. W.

Virtues of the Haversine

In: Sky and telescope, 68-2, 1984
de.lmu.ifi.dbs.elki.math.GeoUtil, de.lmu.ifi.dbs.elki.math.GeoUtil: T. Vincenty

Direct and inverse solutions of geodesics on the ellipsoid with application of nested equations

In: Survey review 23 176, 1975

http://www.ngs.noaa.gov/PUBS_LIB/inverse.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.dimensionsimilarity.HSMDimensionSimilarity: 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

http://dx.doi.org/10.1109/TVCG.2010.242
de.lmu.ifi.dbs.elki.math.dimensionsimilarity.MCEDimensionSimilarity: Diansheng Guo

Coordinating computational and visual approaches for interactive feature selection and multivariate clustering

In: Information Visualization, 2(4)

http://dx.doi.org/10.1057/palgrave.ivs.9500053
de.lmu.ifi.dbs.elki.math.dimensionsimilarity.SURFINGDimensionSimilarity: 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

http://dx.doi.org/10.1109/ICDM.2004.10112
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

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.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

http://dx.doi.org/10.1145/358669.358692
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: 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: 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: 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)

http://dx.doi.org/10.1016/S0895-7177(00)00178-3
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$Instance: 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