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.CanopyPreClustering
A. McCallum, K. Nigam, L.H. Ungar
Efficient Clustering of High Dimensional Data Sets with Application to Reference Matching
In: Proc. 6th ACM SIGKDD international conference on Knowledge discovery and data mining
http://dx.doi.org/10.1145%2F347090.347123
de.lmu.ifi.dbs.elki.algorithm.clustering.DBSCAN, de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.EpsilonNeighborPredicate, de.lmu.ifi.dbs.elki.algorithm.clustering.gdbscan.MinPtsCorePredicate
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.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.affinitypropagation.AffinityPropagationClusteringAlgorithm
B. J. Frey and D. Dueck
Clustering by Passing Messages Between Data Points
In: Science Vol 315
http://dx.doi.org/10.1126/science.1136800
de.lmu.ifi.dbs.elki.algorithm.clustering.biclustering.ChengAndChurch
Y. Cheng, G. M. Church
Biclustering of expression data
In: Proc. 8th International Conference on Intelligent Systems for Molecular Biology (ISMB)
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation.CASH
E. Achtert, C. Böhm, J. David, P. Kröger, A. Zimek
Robust clustering in arbitraily oriented subspaces
In: Proc. 8th SIAM Int. Conf. on Data Mining (SDM'08), Atlanta, GA, 2008
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 Clusters
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.hierarchical.CentroidLinkageMethod, de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.GroupAverageLinkageMethod, de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.WeightedAverageLinkageMethod
A. K. Jain and R. C. Dubes
Algorithms for Clustering Data
In: Algorithms for Clustering Data, Prentice-Hall
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.LinkageMethod, de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.NaiveAgglomerativeHierarchicalClustering
G. N. Lance and W. T. Williams
A general theory of classificatory sorting strategies 1. Hierarchical systems
In: The computer journal 9.4
http://dx.doi.org/ 10.1093/comjnl/9.4.373
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.MedianLinkageMethod
J. C. Gower
A comparison of some methods of cluster analysis
In: Biometrics (1967)
http://www.jstor.org/stable/10.2307/2528417
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.SLINK
R. Sibson
SLINK: An optimally efficient algorithm for the single-link cluster method
In: The Computer Journal 16 (1973), No. 1, p. 30-34.
http://dx.doi.org/10.1093/comjnl/16.1.30
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.SingleLinkageMethod
K. Florek and J. Łukaszewicz and J. Perkal and H. Steinhaus and S. Zubrzycki
Sur la liaison et la division des points d'un ensemble fini
In: Colloquium Mathematicae (Vol. 2, No. 3-4)
de.lmu.ifi.dbs.elki.algorithm.clustering.hierarchical.WardLinkageMethod
J. H. Ward Jr
Hierarchical grouping to optimize an objective function
In: Journal of the American statistical association 58.301
http://dx.doi.org/10.1080/01621459.1963.10500845
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.KMeansBisecting
M. Steinbach, G. Karypis, V. Kumar
A Comparison of Document Clustering Techniques
In: KDD workshop on text mining. Vol. 400. No. 1
de.lmu.ifi.dbs.elki.algorithm.clustering.kmeans.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.DOC
C. M. Procopiuc, M. Jones, P. K. Agarwal, T. M. Murali
A Monte Carlo algorithm for fast projective clustering
In: Proc. ACM SIGMOD Int. Conf. on Management of Data (SIGMOD '02)
http://dx.doi.org/10.1145/564691.564739
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.P3C
Gabriela Moise, Jörg Sander, Martin Ester
P3C: A Robust Projected Clustering Algorithm
In: Proc. Sixth International Conference on Data Mining (ICDM '06)
http://dx.doi.org/10.1109/ICDM.2006.123
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, de.lmu.ifi.dbs.elki.algorithm.outlier.FastABOD, de.lmu.ifi.dbs.elki.algorithm.outlier.LBABOD
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.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.utilities.scaling.outlier.COPOutlierScaling, 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.DWOF
R. Momtaz, N. Mohssen, M. A. Gowayyed
DWOF: A Robust Density-Based OutlierDetection Approach
In: Pattern Recognition and Image Analysis, Proc. 6th Iberian Conference, IbPRIA 2013, Funchal, Madeira, Portugal, 2013.
http://dx.doi.org/10.1007%2F978-3-642-38628-2_61
de.lmu.ifi.dbs.elki.algorithm.outlier.GaussianUniformMixture
Generalization using the likelihood gain as outlier score of
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.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.ODIN, tutorial.outlier.ODIN
V. Hautamäki and I. Kärkkäinen and P Fränti
Outlier detection using k-nearest neighbour graph
In: Proc. 17th Int. Conf. Pattern Recognition, ICPR 2004
http://dx.doi.org/10.1109/ICPR.2004.1334558
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.lof.ALOCI, de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LOCI
S. Papadimitriou, H. Kitagawa, P. B. Gibbons, C. Faloutsos
LOCI: Fast Outlier Detection Using the Local Correlation Integral
In: Proc. 19th IEEE Int. Conf. on Data Engineering (ICDE '03), Bangalore, India, 2003
http://dx.doi.org/10.1109/ICDE.2003.1260802
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.FlexibleLOF, de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LOF
M. M. Breunig, H.-P. Kriegel, R. Ng, and J. Sander
LOF: Identifying Density-Based Local Outliers
In: Proc. 2nd ACM SIGMOD Int. Conf. on Management of Data (SIGMOD '00), Dallas, TX, 2000
http://dx.doi.org/10.1145/342009.335388
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.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.lof.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.lof.LDOF
K. Zhang, M. Hutter, H. Jin
A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data
In: Proc. 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 2009), Bangkok, Thailand, 2009
http://dx.doi.org/10.1007/978-3-642-01307-2_84
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.LoOP
H.-P. Kriegel, P. Kröger, E. Schubert, A. Zimek
LoOP: Local Outlier Probabilities
In: Proceedings of the 18th International Conference on Information and Knowledge Management (CIKM), Hong Kong, China, 2009
http://dx.doi.org/10.1145/1645953.1646195
de.lmu.ifi.dbs.elki.algorithm.outlier.lof.SimplifiedLOF
Erich Schubert, Arthur Zimek, Hans-Peter Kriegel
Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection
In: Data Mining and Knowledge Discovery
http://dx.doi.org/10.1007/s10618-012-0300-z
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.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.datasource.filter.transform.LinearDiscriminantAnalysisFilter
R. A. Fisher
The use of multiple measurements in taxonomic problems
In: Annals of eugenics 7.2 (1936)
http://dx.doi.org/10.1111/j.1469-1809.1936.tb02137.x
de.lmu.ifi.dbs.elki.distance.distancefunction.BrayCurtisDistanceFunction
J. R. Bray and J. T. Curtis
An ordination of the upland forest communities of southern Wisconsin
In: Ecological monographs 27.4
http://dx.doi.org/10.2307/1942268
de.lmu.ifi.dbs.elki.distance.distancefunction.BrayCurtisDistanceFunction
T. Sørensen
A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons
In: Kongelige Danske Videnskabernes Selskab 5 (4)
de.lmu.ifi.dbs.elki.distance.distancefunction.BrayCurtisDistanceFunction
L. R. Dice
Measures of the Amount of Ecologic Association Between Species
In: Ecology 26 (3)
de.lmu.ifi.dbs.elki.distance.distancefunction.CanberraDistanceFunction
G. N. Lance, W. T. Williams
Computer programs for hierarchical polythetic classification (similarity analysis).
In: Computer Journal, Volume 9, Issue 1
http://comjnl.oxfordjournals.org/content/9/1/60.short
de.lmu.ifi.dbs.elki.distance.distancefunction.ClarkDistanceFunction, de.lmu.ifi.dbs.elki.distance.distancefunction.Kulczynski1DistanceFunction, de.lmu.ifi.dbs.elki.distance.distancefunction.LorentzianDistanceFunction, de.lmu.ifi.dbs.elki.distance.similarityfunction.Kulczynski1SimilarityFunction, de.lmu.ifi.dbs.elki.distance.similarityfunction.Kulczynski2SimilarityFunction
M.-M. Deza and E. Deza
Dictionary of distances
In: Dictionary of distances
de.lmu.ifi.dbs.elki.distance.distancefunction.colorhistogram.HSBHistogramQuadraticDistanceFunction
J. R. Smith, S. F. Chang
VisualSEEk: a fully automated content-based image query system
In: Proceedings of the fourth ACM international conference on Multimedia 1997
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.geo.DimensionSelectingLatLngDistanceFunction, de.lmu.ifi.dbs.elki.distance.distancefunction.geo.LatLngDistanceFunction, de.lmu.ifi.dbs.elki.distance.distancefunction.geo.LngLatDistanceFunction, de.lmu.ifi.dbs.elki.math.geodesy.SphereUtil, de.lmu.ifi.dbs.elki.math.geodesy.SphereUtil, de.lmu.ifi.dbs.elki.math.geodesy.SphereUtil
Erich Schubert, Arthur Zimek and Hans-Peter Kriegel
Geodetic Distance Queries on R-Trees for Indexing Geographic Data
In: Advances in Spatial and Temporal Databases - 13th International Symposium, SSTD 2013, Munich, Germany
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.ChiSquaredDistanceFunction, de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.JeffreyDivergenceDistanceFunction
J. Puzicha, J.M. Buhmann, Y. Rubner, C. Tomasi
Empirical evaluation of dissimilarity measures for color and texture
In: Proc. 7th IEEE International Conference on Computer Vision
http://dx.doi.org/10.1109/ICCV.1999.790412
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.KullbackLeiblerDivergenceAsymmetricDistanceFunction, de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.KullbackLeiblerDivergenceReverseAsymmetricDistanceFunction
S. Kullback
Information theory and statistics
In: Information theory and statistics, Courier Dover Publications, 1997.
de.lmu.ifi.dbs.elki.distance.distancefunction.probabilistic.SqrtJensenShannonDivergenceDistanceFunction
D. M. Endres, J. E. Schindelin
A new metric for probability distributions
In: IEEE Transactions on Information Theory, 49(7)
http://dx.doi.org/10.1109/TIT.2003.813506
de.lmu.ifi.dbs.elki.distance.distancefunction.strings.LevenshteinDistanceFunction, de.lmu.ifi.dbs.elki.distance.distancefunction.strings.NormalizedLevenshteinDistanceFunction
V. I. Levenshtein
Binary codes capable of correcting deletions, insertions and reversals.
In: Soviet physics doklady. Vol. 10. 1966.
de.lmu.ifi.dbs.elki.distance.distancefunction.timeseries.DTWDistanceFunction
Berndt, D. and Clifford, J.
Using dynamic time warping to find patterns in time series
In: AAAI-94 Workshop on Knowledge Discovery in Databases, 1994
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.distance.similarityfunction.JaccardPrimitiveSimilarityFunction
P. Jaccard
Étude comparative de la distribution florale dans une portion des Alpes et des Jura
In: Bulletin del la Société Vaudoise des Sciences Naturelles
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
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.lsh.hashfamilies.EuclideanHashFunctionFamily, de.lmu.ifi.dbs.elki.index.lsh.hashfamilies.ManhattanHashFunctionFamily, de.lmu.ifi.dbs.elki.index.lsh.hashfunctions.MultipleProjectionsLocalitySensitiveHashFunction
M. Datar and N. Immorlica and P. Indyk and V. S. Mirrokni
Locality-sensitive hashing scheme based on p-stable distributions
In: Proc. 20th annual symposium on Computational geometry
http://dx.doi.org/10.1145/997817.997857
de.lmu.ifi.dbs.elki.index.preprocessed.knn.RandomSampleKNNPreprocessor
A. Zimek and M. Gaudet and R. J. G. B. Campello and J. Sander
Subsampling for Efficient and Effective Unsupervised Outlier Detection Ensembles
In: Proc. 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '13
de.lmu.ifi.dbs.elki.index.projected.PINN
T. de Vries, S. Chawla, M. E. Houle
Finding local anomalies in very high dimensional space
In: Proc. IEEE 10th International Conference on Data Mining (ICDM)
http://dx.doi.org/10.1109/ICDM.2010.151
de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.mtree.MTree, de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.insert.MinimumEnlargementInsert, de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.MLBDistSplit, de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.MMRadSplit, de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.MRadSplit, de.lmu.ifi.dbs.elki.index.tree.metrical.mtreevariants.strategies.split.RandomSplit
P. Ciaccia, M. Patella, P. Zezula
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
In: VLDB'97, Proceedings of 23rd International Conference on Very Large Data Bases, August 25-29, 1997, Athens, Greece
http://www.vldb.org/conf/1997/P426.PDF
de.lmu.ifi.dbs.elki.index.tree.spatial.kd.MinimalisticMemoryKDTree, 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.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. of 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.MeanVariance
D.H.D. West
Updating Mean and Variance Estimates: An Improved Method
In: Communications of the ACM, Volume 22 Issue 9
de.lmu.ifi.dbs.elki.math.StatisticalMoments
T. B. Terriberry
Computing Higher-Order Moments Online
In: Online - Technical Note
http://people.xiph.org/~tterribe/notes/homs.html
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.HiCSDimensionSimilarity, de.lmu.ifi.dbs.elki.math.dimensionsimilarity.SURFINGDimensionSimilarity, de.lmu.ifi.dbs.elki.math.dimensionsimilarity.SlopeDimensionSimilarity, de.lmu.ifi.dbs.elki.math.dimensionsimilarity.SlopeInversionDimensionSimilarity
Elke Achtert, Hans-Peter Kriegel, Erich Schubert, Arthur Zimek
Interactive Data Mining with 3D-Parallel-Coordinate-Trees
In: Proc. of the 2013 ACM International Conference on Management of Data (SIGMOD)
http://dx.doi.org/10.1145/2463676.2463696
de.lmu.ifi.dbs.elki.math.dimensionsimilarity.MCEDimensionSimilarity
D. 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.geodesy.SphereUtil
Ed Williams
Aviation Formulary
In:
http://williams.best.vwh.net/avform.htm
de.lmu.ifi.dbs.elki.math.geodesy.SphereUtil, de.lmu.ifi.dbs.elki.math.geodesy.SphereUtil, de.lmu.ifi.dbs.elki.math.geodesy.SphereUtil, de.lmu.ifi.dbs.elki.math.geodesy.SphereUtil
T. Vincenty
Direct and inverse solutions of geodesics on the ellipsoid with application of nested equations
In: Survey review 23 176, 1975
http://www.ngs.noaa.gov/PUBS_LIB/inverse.pdf
de.lmu.ifi.dbs.elki.math.geodesy.SphereUtil, de.lmu.ifi.dbs.elki.math.geodesy.SphereUtil
Sinnott, R. W.
Virtues of the Haversine
In: Sky and telescope, 68-2, 1984
de.lmu.ifi.dbs.elki.math.geometry.GrahamScanConvexHull2D
Paul Graham
An Efficient Algorithm for Determining the Convex Hull of a Finite Planar Set
In: Information Processing Letters 1
de.lmu.ifi.dbs.elki.math.geometry.PrimsMinimumSpanningTree
R. C. Prim
Shortest connection networks and some generalizations
In: Bell System Technical Journal, 36 (1957)
de.lmu.ifi.dbs.elki.math.geometry.SweepHullDelaunay2D
David Sinclair
S-hull: a fast sweep-hull routine for Delaunay triangulation
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.linearalgebra.randomprojections.AchlioptasRandomProjectionFamily
Dimitris Achlioptas
Database-friendly random projections: Johnson-Lindenstrauss with binary coins
In: Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
http://dx.doi.org/10.1145/375551.375608
de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections.CauchyRandomProjectionFamily, de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections.GaussianRandomProjectionFamily
M. Datar and N. Immorlica and P. Indyk and V. S. Mirrokni
Locality-sensitive hashing scheme based on p-stable distributions
In: Proceedings of the 20th annual symposium on Computational geometry
http://dx.doi.org/10.1145/997817.997857
de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections.RandomSubsetProjectionFamily
L. Breiman
Bagging predictors
In: Machine learning 24.2
http://dx.doi.org/10.1007/BF00058655
de.lmu.ifi.dbs.elki.math.spacefillingcurves.HilbertSpatialSorter
D. Hilbert
Über die stetige Abbildung einer Linie auf ein Flächenstück
In: Mathematische Annalen, 38(3)
de.lmu.ifi.dbs.elki.math.spacefillingcurves.PeanoSpatialSorter
G. Peano
Sur une courbe, qui remplit toute une aire plane
In: Mathematische Annalen, 36(1)
de.lmu.ifi.dbs.elki.math.statistics.ProbabilityWeightedMoments, de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.GeneralizedExtremeValueLMMEstimator
J.R.M. Hosking, J. R. Wallis, and E. F. Wood
Estimation of the generalized extreme-value distribution by the method of probability-weighted moments.
In: Technometrics 27.3
http://dx.doi.org/10.1080/00401706.1985.10488049
de.lmu.ifi.dbs.elki.math.statistics.distribution.ChiSquaredDistribution, de.lmu.ifi.dbs.elki.math.statistics.distribution.GammaDistribution, de.lmu.ifi.dbs.elki.math.statistics.distribution.GammaDistribution
D.J. Best, D. E. Roberts
Algorithm AS 91: The percentage points of the $\chi^2$ distribution
In: Journal of the Royal Statistical Society. Series C (Applied Statistics)
de.lmu.ifi.dbs.elki.math.statistics.distribution.GammaDistribution
J. M. Bernando
Algorithm AS 103: Psi (Digamma) Function
In: Statistical Algorithms
de.lmu.ifi.dbs.elki.math.statistics.distribution.HaltonUniformDistribution
Wang, X. and Hickernell, F.J.
Randomized halton sequences
In: Mathematical and Computer Modelling Vol. 32 (7)
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
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.math.statistics.distribution.estimator.CauchyMADEstimator, de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.ExponentialMADEstimator, de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.GumbelMADEstimator, de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LaplaceMADEstimator, de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LogLogisticMADEstimator, de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.RayleighMADEstimator, de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.UniformMADEstimator, de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.WeibullLogMADEstimator
D. J. Olive
Applied Robust Statistics
In: Applied Robust Statistics
http://lagrange.math.siu.edu/Olive/preprints.htm
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.EMGOlivierNorbergEstimator
J. Olivier, M. M. Norberg
Positively skewed data: Revisiting the Box-Cox power transformation
In: International Journal of Psychological Research Vol. 3 No. 1
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.ExponentialLMMEstimator, de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.GammaLMMEstimator, de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.GeneralizedLogisticAlternateLMMEstimator, de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.GumbelLMMEstimator, de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LogNormalLMMEstimator, de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LogisticLMMEstimator, de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.NormalLMMEstimator, de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.SkewGNormalLMMEstimator
J.R.M. Hosking
Fortran routines for use with the method of L-moments Version 3.03
In: IBM Research Technical Report
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.ExponentialMedianEstimator, de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LogisticMADEstimator
D. J. Olive
Robust Estimators for Transformed Location Scale Families
In:
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.GammaChoiWetteEstimator, de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LogGammaChoiWetteEstimator
S. C. Choi, R. Wette
Maximum likelihood estimation of the parameters of the gamma distribution and their bias
In: Technometrics
http://www.jstor.org/stable/10.2307/1266892
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.GammaMADEstimator
J. Chen. H. Rubin
Bounds for the difference between median and mean of Gamma and Poisson distributions
In: Statist. Probab. Lett., 4
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.GammaMOMEstimator
G. Casella, R. L. Berger
Statistical inference. Vol. 70
In: Statistical inference. Vol. 70
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LaplaceMLEEstimator
R. M. Norton
The Double Exponential Distribution: Using Calculus to Find a Maximum Likelihood Estimator
In: The American Statistician 38 (2)
http://dx.doi.org/10.2307%2F2683252
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LogNormalBilkovaLMMEstimator
D. Bílková
Lognormal distribution and using L-moment method for estimating its parameters
In: Int. Journal of Mathematical Models and Methods in Applied Sciences (NAUN)
http://www.naun.org/multimedia/NAUN/m3as/17-079.pdf
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.LogNormalLogMADEstimator, de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.NormalMADEstimator
F. R. Hampel
The Influence Curve and Its Role in Robust Estimation
In: Journal of the American Statistical Association, June 1974, Vol. 69, No. 346
http://www.jstor.org/stable/10.2307/2285666
de.lmu.ifi.dbs.elki.math.statistics.distribution.estimator.meta.WinsorisingEstimator
C. Hastings, F. Mosteller, J. W. Tukey, C. P. Winsor
Low moments for small samples: a comparative study of order statistics
In: The Annals of Mathematical Statistics, 18(3)
http://dx.doi.org/10.1214/aoms/1177730388
de.lmu.ifi.dbs.elki.math.statistics.kernelfunctions.KernelDensityFunction
J.S. Marron, D. Nolan
Canonical kernels for density estimation
In: Statistics & Probability Letters, Volume 7, Issue 3
http://dx.doi.org/10.1016/0167-7152(88)90050-8
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.COPOutlierScaling, 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.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.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
tutorial.clustering.NaiveAgglomerativeHierarchicalClustering3, tutorial.clustering.NaiveAgglomerativeHierarchicalClustering4
R. M. Cormack
A Review of Classification
In: Journal of the Royal Statistical Society. Series A, Vol. 134, No. 3
http://www.jstor.org/stable/2344237