Publications implemented or referenced by ELKI

The following publications are cited by classes in ELKI (as of ELKI 0.5 pre):

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

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

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

By: E. Achtert, C. Böhm, H.-P. Kriegel, P. Kröger, A. Zimek
Deriving Quantitative Dependencies for Correlation Clusters
In: Proc. 12th Int. Conf. on Knowledge Discovery and Data Mining (KDD '06), Philadelphia, PA 2006.
Online:  http://dx.doi.org/10.1145/1150402.1150408

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

By: 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
Online:  http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.71.1980

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

By: E. Achtert, C. Böhm, P. Kröger
DeLiClu: Boosting Robustness, Completeness, Usability, and Efficiency of Hierarchical Clustering by a Closest Pair Ranking
In: Proc. 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006), Singapore, 2006
Online:  http://dx.doi.org/10.1007/11731139_16

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

By: A. P. Dempster, N. M. Laird, D. B. Rubin
Maximum Likelihood from Incomplete Data via the EM algorithm
In: Journal of the Royal Statistical Society, Series B, 39(1), 1977, pp. 1-31
Online:  http://www.jstor.org/stable/2984875

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

By: M. Ankerst, M. Breunig, H.-P. Kriegel, and J. Sander
OPTICS: Ordering Points to Identify the Clustering Structure
In: Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD '99)
Online:  http://dx.doi.org/10.1145/304181.304187

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

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

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

By: L. Ertöz, M. Steinbach, V. Kumar
Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data
In: Proc. of SIAM Data Mining (SDM), 2003
Online:  http://www.siam.org/meetings/sdm03/proceedings/sdm03_05.pdf

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

By: E. Achtert, C. Böhm, J. David, P. Kröger, A. Zimek
Robust clustering in arbitraily oriented subspaces
In: Proc. 8th SIAM Int. Conf. on Data Mining (SDM'08), Atlanta, GA, 2008
Online:  http://www.siam.org/proceedings/datamining/2008/dm08_69_AchtertBoehmDavidKroegerZimek.pdf

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

By: 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
Online:  http://www.siam.org/proceedings/datamining/2007/dm07_037achtert.pdf

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

By: E. Achtert, C. Böhm, H.-P. Kriegel, P. Kröger, and A. Zimek
On Exploring Complex Relationships of Correlation Clusters
In: Proc. 19th International Conference on Scientific and Statistical Database Management (SSDBM 2007), Banff, Canada, 2007
Online:  http://dx.doi.org/10.1109/SSDBM.2007.21

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

By: C. Böhm, K. Kailing, P. Kröger, A. Zimek
Computing Clusters of Correlation Connected Objects
In: Proc. ACM SIGMOD Int. Conf. on Management of Data, Paris, France, 2004, 455-466
Online:  http://dx.doi.org/10.1145/1007568.1007620

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

By: E. Achtert, C. Böhm, P. Kröger, A. Zimek
Mining Hierarchies of Correlation Clusterse
In: Proc. Int. Conf. on Scientific and Statistical Database Management (SSDBM'06), Vienna, Austria, 2006
Online:  http://dx.doi.org/10.1109/SSDBM.2006.35

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

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

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

By: C. C. Aggarwal, P. S. Yu
Finding Generalized Projected Clusters in High Dimensional Spaces
In: Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD '00)
Online:  http://dx.doi.org/10.1145/342009.335383

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

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

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

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

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

By: D. Arthur, S. Vassilvitskii
k-means++: the advantages of careful seeding
In: Proc. of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2007
Online:  http://dx.doi.org/10.1145/1283383.1283494

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

By: R. Agrawal, J. Gehrke, D. Gunopulos, P. Raghavan
Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications
In: Proc. SIGMOD Conference, Seattle, WA, 1998
Online:  http://dx.doi.org/10.1145/276304.276314

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

By: E. Achtert, C. Böhm, H.-P. Kriegel, P. Kröger, I. Müller-Gorman, A. Zimek
Detection and Visualization of Subspace Cluster Hierarchies
In: Proc. 12th International Conference on Database Systems for Advanced Applications (DASFAA), Bangkok, Thailand, 2007
Online:  http://dx.doi.org/10.1007/978-3-540-71703-4_15

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

By: E. Achtert, C. Böhm, H.-P. Kriegel, P. Kröger, I. Müller-Gorman, A. Zimek
Finding Hierarchies of Subspace Clusters
In: Proc. 10th Europ. Conf. on Principles and Practice of Knowledge Discovery in Databases (PKDD'06), Berlin, Germany, 2006
Online:  http://www.dbs.ifi.lmu.de/Publikationen/Papers/PKDD06-HiSC.pdf

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

By: C. C. Aggarwal, C. Procopiuc, J. L. Wolf, P. S. Yu, J. S. Park
Fast Algorithms for Projected Clustering
In: Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD '99)
Online:  http://dx.doi.org/10.1145/304181.304188

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

By: C. Böhm, K. Kailing, H.-P. Kriegel, P. Kröger
Density Connected Clustering with Local Subspace Preferences
In: Proc. 4th IEEE Int. Conf. on Data Mining (ICDM'04), Brighton, UK, 2004
Online:  http://dx.doi.org/10.1109/ICDM.2004.10087

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

By: K. Kailing, H.-P. Kriegel, P. Kröger
Density connected Subspace Clustering for High Dimensional Data.
In: Proc. SIAM Int. Conf. on Data Mining (SDM'04), Lake Buena Vista, FL, 2004

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

By: 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
Online:  http://dx.doi.org/10.1145/1401890.1401946

de.lmu.ifi.dbs.elki.algorithm.outlier.AbstractAggarwalYuOutlier,
de.lmu.ifi.dbs.elki.algorithm.outlier.AggarwalYuEvolutionary,
de.lmu.ifi.dbs.elki.algorithm.outlier.AggarwalYuNaive

By: 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
Online:  http://dx.doi.org/10.1145/375663.375668

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

By: E.M. Knorr, R. T. Ng
Algorithms for Mining Distance-Based Outliers in Large Datasets
In: Procs Int. Conf. on Very Large Databases (VLDB'98), New York, USA, 1998

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

Generalization of a method proposed in
By: E.M. Knorr, R. T. Ng
Algorithms for Mining Distance-Based Outliers in Large Datasets
In: Procs Int. Conf. on Very Large Databases (VLDB'98), New York, USA, 1998

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

Generalization using the likelihood gain as outlier score of
By: 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.INFLO

By: 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
Online:  http://dx.doi.org/10.1007/11731139_68

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

By: 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
Online:  http://dx.doi.org/10.1145/342009.335437

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

By: 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
Online:  http://dx.doi.org/10.1007/3-540-45681-3_2

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

By: 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
Online:  http://dx.doi.org/10.1007/978-3-642-01307-2_84

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

By: 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
Online:  http://dx.doi.org/10.1109/ICDE.2003.1260802

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

By: 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
Online:  http://dx.doi.org/10.1145/342009.335388

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

By: 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
Online:  http://dx.doi.org/10.1145/1645953.1646195

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

By: M. M. Breunig, H.-P. Kriegel, R. Ng, and J. Sander
OPTICS-OF: Identifying Local Outliers
In: Proc. of the 3rd European Conference on Principles of Knowledge Discovery and Data Mining (PKDD), Prague, Czech Republic
Online:  http://springerlink.metapress.com/content/76bx6413gqb4tvta/

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

By: Y. Pei, O.R. Zaiane, Y. Gao
An Efficient Reference-based Approach to Outlier Detection in Large Datasets
In: Proc. 19th IEEE Int. Conf. on Data Engineering (ICDE '03), Bangalore, India, 2003
Online:  http://dx.doi.org/10.1109/ICDM.2006.17

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

By: 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
Online:  http://dx.doi.org/10.1007/978-3-642-01307-2

de.lmu.ifi.dbs.elki.algorithm.outlier.meta.FeatureBagging

By: A. Lazarevic, V. Kumar
Feature Bagging for Outlier Detection
In: Proc. of the 11th ACM SIGKDD international conference on Knowledge discovery in data mining
Online:  http://dx.doi.org/10.1145/1081870.1081891

de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuGLSBackwardSearchAlgorithm

By: F. Chen and C.-T. Lu and A. P. Boedihardjo
GLS-SOD: A Generalized Local Statistical Approach for Spatial Outlier Detection
In: Proc. 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Online:  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

By: 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
Online:  http://dx.doi.org/10.1109/TAI.2003.1250179

de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuMedianAlgorithm

By: C.-T. Lu and D. Chen and Y. Kou
Algorithms for Spatial Outlier Detection
In: Proc. 3rd IEEE International Conference on Data Mining
Online:  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

By: S. Shekhar and C.-T. Lu and P. Zhang
A Unified Approach to Detecting Spatial Outliers
In: GeoInformatica 7-2, 2003
Online:  http://dx.doi.org/10.1023/A:1023455925009

de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.CTLuRandomWalkEC

By: X. Liu and C.-T. Lu and F. Chen
Spatial outlier detection: random walk based approaches
In: Proc. 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2010
Online:  http://dx.doi.org/10.1145/1869790.1869841

de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.SLOM

By: Sanjay Chawla and Pei Sun
SLOM: a new measure for local spatial outliers
In: Knowledge and Information Systems 2005
Online:  http://rp-www.cs.usyd.edu.au/~chawlarg/papers/KAIS_online.pdf

de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.SOF

By: Huang, T., Qin, X.
Detecting outliers in spatial database
In: Proc. 3rd International Conference on Image and Graphics
Online:  http://dx.doi.org/10.1109/ICIG.2004.53

de.lmu.ifi.dbs.elki.algorithm.outlier.spatial.TrimmedMeanApproach

By: Tianming Hu and Sam Yuan Sung
A trimmed mean approach to finding spatial outliers
In: Intelligent Data Analysis, Volume 8, 2004
Online:  http://iospress.metapress.com/content/PLVLT6431DVNJXNK

de.lmu.ifi.dbs.elki.application.greedyensemble.ComputeKNNOutlierScores,
de.lmu.ifi.dbs.elki.application.greedyensemble.GreedyEnsembleExperiment,
de.lmu.ifi.dbs.elki.application.greedyensemble.VisualizePairwiseGainMatrix

By: 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

By: 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
Online:  http://dx.doi.org/10.1007/978-3-642-02982-0_35

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

By: G. N. Lance, W. T. Williams
Computer programs for hierarchical polythetic classification (similarity analysis).
In: Computer Journal, Volume 9, Issue 1
Online:  http://comjnl.oxfordjournals.org/content/9/1/60.short

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

By: 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
Online:  http://dx.doi.org/10.1145/244130.244151

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

By: 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

By: 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
Online:  http://dx.doi.org/10.1109/34.391417

de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries.DTWDistanceFunction

By: 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
Online:  http://www.aaai.org/Papers/Workshops/1994/WS-94-03/WS94-03-031.pdf

de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries.EDRDistanceFunction

By: 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
Online:  http://dx.doi.org/10.1145/1066157.1066213

de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries.ERPDistanceFunction

By: 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
Online:  http://www.vldb.org/conf/2004/RS21P2.PDF

de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries.LCSSDistanceFunction

By: 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
Online:  http://dx.doi.org/10.1145/956750.956777

de.lmu.ifi.dbs.elki.evaluation.clustering.BCubed

By: 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
Online:  http://dx.doi.org/10.3115/980451.980859

de.lmu.ifi.dbs.elki.evaluation.clustering.EditDistance

By: Pantel, P. and Lin, D.
Document clustering with committees
In: Proc. 25th ACM SIGIR conference on Research and development in information retrieval
Online:  http://dx.doi.org/10.1145/564376.564412

de.lmu.ifi.dbs.elki.evaluation.clustering.Entropy

By: Meilă, M.
Comparing clusterings by the variation of information
In: Learning theory and kernel machines Volume 2777/2003
Online:  http://dx.doi.org/10.1007/978-3-540-45167-9_14

de.lmu.ifi.dbs.elki.evaluation.clustering.Entropy

By: 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
Online:  http://dx.doi.org/10.1145/1553374.1553511

de.lmu.ifi.dbs.elki.evaluation.clustering.PairCounting

By: 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

By: Rand, W. M.
Objective Criteria for the Evaluation of Clustering Methods
In: Journal of the American Statistical Association, Vol. 66 Issue 336
Online:  http://www.jstor.org/stable/10.2307/2284239

de.lmu.ifi.dbs.elki.evaluation.clustering.SetMatchingPurity

By: Meilă, M
Comparing clusterings
In: University of Washington, Seattle, Technical Report 418, 2002
Online:  http://www.stat.washington.edu/mmp/www.stat.washington.edu/mmp/Papers/compare-colt.pdf

de.lmu.ifi.dbs.elki.evaluation.clustering.SetMatchingPurity

By: Steinbach, M. and Karypis, G. and Kumar, V. and others
A comparison of document clustering techniques
In: KDD workshop on text mining, 2000
Online:  http://www-users.itlabs.umn.edu/~karypis/publications/Papers/PDF/doccluster.pdf

de.lmu.ifi.dbs.elki.evaluation.clustering.SetMatchingPurity

By: 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
Online:  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

By: 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
Online:  http://elki.dbs.ifi.lmu.de/wiki/PairSegments

de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree.MTree

By: 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
Online:  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

By: G. R. Hjaltason, H. Samet
Ranking in spatial databases
In: Advances in Spatial Databases - 4th Symposium, SSD'95
Online:  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

By: J. Kuan, P. Lewis
Fast k nearest neighbour search for R-tree family
In: Proc. Int. Conf Information, Communications and Signal Processing, ICICS 1997
Online:  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

By: 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
Online:  http://dx.doi.org/10.1145/93597.98741

de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.bulk.OneDimSortBulkSplit

By: Roussopoulos, N. and Leifker, D.
Direct spatial search on pictorial databases using packed R-trees
In: ACM SIGMOD Record 14-4
Online:  http://dx.doi.org/10.1145/971699.318900

de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.bulk.SortTileRecursiveBulkSplit

By: 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
Online:  http://dx.doi.org/10.1109/ICDE.1997.582015

de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.bulk.SpatialSortBulkSplit

By: Kamel, I. and Faloutsos, C.
On packing R-trees
In: Proc. 2of the second international conference on Information and knowledge management
Online:  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

By: Antonin Guttman
R-Trees: A Dynamic Index Structure For Spatial Searching
In: Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Online:  http://dx.doi.org/10.1145/971697.602266

de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.split.AngTanLinearSplit

By: C. H. Ang and T. C. Tan
New linear node splitting algorithm for R-trees
In: Proceedings of the 5th International Symposium on Advances in Spatial Databases
Online:  http://dx.doi.org/10.1007/3-540-63238-7_38

de.lmu.ifi.dbs.elki.index.tree.spatial.rstarvariants.strategies.split.GreeneSplit

By: Diane Greene
An implementation and performance analysis of spatial data access methods
In: Proceedings of the Fifth International Conference on Data Engineering
Online:  http://dx.doi.org/10.1109/ICDE.1989.47268

de.lmu.ifi.dbs.elki.index.vafile.VAFile

By: Weber, R. and Blott, S.
An approximation based data structure for similarity search
In: Report TR1997b, ETH Zentrum, Zurich, Switzerland
Online:  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

By: 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

By: 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

By: 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

By: 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
Online:  http://dx.doi.org/10.1007/978-3-540-69497-7_27

de.lmu.ifi.dbs.elki.math.spacefillingcurves.BinarySplitSpatialSorter

By: J. L. Bentley
Multidimensional binary search trees used for associative searching
In: Communications of the ACM, Vol. 18 Issue 9, Sept. 1975
Online:  http://dx.doi.org/10.1145/361002.361007

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

By: E. Achtert, A. Hettab, H.-P. Kriegel, E. Schubert, A. Zimek
Spatial Outlier Detection: Data, Algorithms, Visualizations
In: Proc. 12th International Symposium on Spatial and Temporal Databases (SSTD), Minneapolis, MN, 2011

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

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

de.lmu.ifi.dbs.elki.utilities.scaling.outlier.MinusLogGammaScaling,
de.lmu.ifi.dbs.elki.utilities.scaling.outlier.MinusLogStandardDeviationScaling,
de.lmu.ifi.dbs.elki.utilities.scaling.outlier.MultiplicativeInverseScaling,
de.lmu.ifi.dbs.elki.utilities.scaling.outlier.OutlierGammaScaling,
de.lmu.ifi.dbs.elki.utilities.scaling.outlier.OutlierMinusLogScaling,
de.lmu.ifi.dbs.elki.utilities.scaling.outlier.SqrtStandardDeviationScaling,
de.lmu.ifi.dbs.elki.utilities.scaling.outlier.StandardDeviationScaling

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

By: J. Gao, P.-N. Tan
Converting Output Scores from Outlier Detection Algorithms into Probability Estimates
In: Proc. Sixth International Conference on Data Mining, 2006. ICDM'06.
Online:  http://dx.doi.org/10.1109/ICDM.2006.43

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

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

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

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