Correlation clustering
暂无分享,去创建一个
[1] Robert C. Bolles,et al. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.
[2] Geoffrey I. Webb. Discovering associations with numeric variables , 2001, KDD '01.
[3] John L. Pfaltz,et al. What Constitutes a Scientific Database? , 2007, 19th International Conference on Scientific and Statistical Database Management (SSDBM 2007).
[4] Christos Faloutsos,et al. Estimating the Selectivity of Spatial Queries Using the 'Correlation' Fractal Dimension , 1995, VLDB.
[5] Anil K. Jain,et al. Data clustering: a review , 1999, CSUR.
[6] Philip S. Yu,et al. Finding generalized projected clusters in high dimensional spaces , 2000, SIGMOD '00.
[7] Robert M. Haralick,et al. Model-based linear manifold clustering , 2008 .
[8] Stephen D. Bay,et al. Mining distance-based outliers in near linear time with randomization and a simple pruning rule , 2003, KDD '03.
[9] Raymond T. Ng,et al. Finding Intensional Knowledge of Distance-Based Outliers , 1999, VLDB.
[10] Christos Faloutsos,et al. Deflating the dimensionality curse using multiple fractal dimensions , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).
[11] Alexander J. Smola,et al. Learning with kernels , 1998 .
[12] George M. Church,et al. Biclustering of Expression Data , 2000, ISMB.
[13] Dirk Husmeier,et al. Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks , 2003, Bioinform..
[14] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[15] Ramakrishnan Srikant,et al. Mining quantitative association rules in large relational tables , 1996, SIGMOD '96.
[16] Philip S. Yu,et al. /spl delta/-clusters: capturing subspace correlation in a large data set , 2002, Proceedings 18th International Conference on Data Engineering.
[17] T. M. Murali,et al. Extracting Conserved Gene Expression Motifs from Gene Expression Data , 2002, Pacific Symposium on Biocomputing.
[18] Sanjay Chawla,et al. On local spatial outliers , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).
[19] Thomas G. Dietterich. What is machine learning? , 2020, Archives of Disease in Childhood.
[20] Anthony K. H. Tung,et al. CURLER: finding and visualizing nonlinear correlation clusters , 2005, SIGMOD '05.
[21] Philip S. Yu,et al. Clustering by pattern similarity in large data sets , 2002, SIGMOD '02.
[22] Inderjit S. Dhillon,et al. Minimum Sum-Squared Residue Co-Clustering of Gene Expression Data , 2004, SDM.
[23] Hans-Jörg Schek,et al. A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces , 1998, VLDB.
[24] Wei Wang,et al. OP-cluster: clustering by tendency in high dimensional space , 2003, Third IEEE International Conference on Data Mining.
[25] Hans-Peter Kriegel,et al. A General Framework for Increasing the Robustness of PCA-Based Correlation Clustering Algorithms , 2008, SSDBM.
[26] Christian Böhm,et al. Density connected clustering with local subspace preferences , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).
[27] Elke Achtert,et al. ELKI in Time: ELKI 0.2 for the Performance Evaluation of Distance Measures for Time Series , 2009, SSTD.
[28] P. Rousseeuw,et al. Computing depth contours of bivariate point clouds , 1996 .
[29] Hans-Peter Kriegel,et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.
[30] Roded Sharan,et al. Biclustering Algorithms: A Survey , 2007 .
[31] Martin Ester,et al. Robust projected clustering , 2007, Knowledge and Information Systems.
[32] Christian Böhm,et al. Dynamically Optimizing High-Dimensional Index Structures , 2000, EDBT.
[33] Ira Assent,et al. DUSC: Dimensionality Unbiased Subspace Clustering , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).
[34] Shigeo Abe DrEng. Pattern Classification , 2001, Springer London.
[35] Dimitrios Gunopulos,et al. Automatic subspace clustering of high dimensional data for data mining applications , 1998, SIGMOD '98.
[36] Padhraic Smyth,et al. Knowledge Discovery and Data Mining: Towards a Unifying Framework , 1996, KDD.
[37] Prabhakar Raghavan,et al. A Linear Method for Deviation Detection in Large Databases , 1996, KDD.
[38] Dimitrios Gunopulos,et al. Subspace Clustering of High Dimensional Data , 2004, SDM.
[39] Christian Böhm,et al. Computing Clusters of Correlation Connected objects , 2004, SIGMOD '04.
[40] Inderjit S. Dhillon,et al. Co-clustering documents and words using bipartite spectral graph partitioning , 2001, KDD '01.
[41] Hans-Peter Kriegel,et al. Fast nearest neighbor search in high-dimensional space , 1998, Proceedings 14th International Conference on Data Engineering.
[42] Christos Faloutsos,et al. LOCI: fast outlier detection using the local correlation integral , 2003, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405).
[43] Elke Achtert,et al. Global Correlation Clustering Based on the Hough Transform , 2008, Stat. Anal. Data Min..
[44] Azriel Rosenfeld,et al. Picture Processing by Computer , 1969, CSUR.
[45] Ben Taskar,et al. Rich probabilistic models for gene expression , 2001, ISMB.
[46] Clara Pizzuti,et al. Fast Outlier Detection in High Dimensional Spaces , 2002, PKDD.
[47] Myoung-Ho Kim,et al. FINDIT: a fast and intelligent subspace clustering algorithm using dimension voting , 2004, Inf. Softw. Technol..
[48] Sridhar Ramaswamy,et al. Efficient algorithms for mining outliers from large data sets , 2000, SIGMOD '00.
[49] L. Beran,et al. [Formal concept analysis]. , 1996, Casopis lekaru ceskych.
[50] Sven Bergmann,et al. Defining transcription modules using large-scale gene expression data , 2004, Bioinform..
[51] Osmar R. Zaïane,et al. A Nonparametric Outlier Detection for Effectively Discovering Top-N Outliers from Engineering Data , 2006, PAKDD.
[52] A. Madansky. Identification of Outliers , 1988 .
[53] Alok N. Choudhary,et al. Adaptive Grids for Clustering Massive Data Sets , 2001, SDM.
[54] G. Getz,et al. Coupled two-way clustering analysis of gene microarray data. , 2000, Proceedings of the National Academy of Sciences of the United States of America.
[55] Hans-Peter Kriegel,et al. Future trends in data mining , 2007, Data Mining and Knowledge Discovery.
[56] Heng Tao Shen,et al. Principal Component Analysis , 2009, Encyclopedia of Biometrics.
[57] Jinyan Li,et al. Distance Based Subspace Clustering with Flexible Dimension Partitioning , 2007, 2007 IEEE 23rd International Conference on Data Engineering.
[58] Philip S. Yu,et al. MaPle: a fast algorithm for maximal pattern-based clustering , 2003, Third IEEE International Conference on Data Mining.
[59] Man Lung Yiu,et al. Frequent-pattern based iterative projected clustering , 2003, Third IEEE International Conference on Data Mining.
[60] Nimrod Megiddo,et al. Discovery-Driven Exploration of OLAP Data Cubes , 1998, EDBT.
[61] Elke Achtert,et al. Robust Clustering in Arbitrarily Oriented Subspaces , 2008, SDM.
[62] Christos Faloutsos,et al. How to Use the Fractal Dimension to Find Correlations between Attributes , 2002 .
[63] Shin'ichi Satoh,et al. The SR-tree: an index structure for high-dimensional nearest neighbor queries , 1997, SIGMOD '97.
[64] Robert M. Haralick,et al. Mining Subspace Correlations , 2007, 2007 IEEE Symposium on Computational Intelligence and Data Mining.
[65] T. M. Murali,et al. A Monte Carlo algorithm for fast projective clustering , 2002, SIGMOD '02.
[66] M. Braga,et al. Exploratory Data Analysis , 2018, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..
[67] Boris Mirkin,et al. Mathematical Classification and Clustering , 1996 .
[68] Robert M. Haralick,et al. Linear Manifold Clustering , 2005, MLDM.
[69] H. O. Posten. Multidimensional Gaussian Distributions , 1964 .
[70] Philip S. Yu,et al. Outlier detection for high dimensional data , 2001, SIGMOD '01.
[71] Elke Achtert,et al. Mining Hierarchies of Correlation Clusters , 2006, 18th International Conference on Scientific and Statistical Database Management (SSDBM'06).
[72] Christos Faloutsos,et al. The TV-tree: An index structure for high-dimensional data , 1994, The VLDB Journal.
[73] Heikki Mannila,et al. Principles of Data Mining , 2001, Undergraduate Topics in Computer Science.
[74] Mohammed J. Zaki,et al. SCHISM: a new approach for interesting subspace mining , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).
[75] Hans-Peter Kriegel,et al. Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications , 1998, Data Mining and Knowledge Discovery.
[76] Man Lung Yiu,et al. Iterative projected clustering by subspace mining , 2005, IEEE Transactions on Knowledge and Data Engineering.
[77] Hans-Peter Kriegel,et al. OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.
[78] Aidong Zhang,et al. Cluster analysis for gene expression data: a survey , 2004, IEEE Transactions on Knowledge and Data Engineering.
[79] Elke Achtert,et al. On Exploring Complex Relationships of Correlation Clusters , 2007, 19th International Conference on Scientific and Statistical Database Management (SSDBM 2007).
[80] Andrea Califano,et al. Analysis of Gene Expression Microarrays for Phenotype Classification , 2000, ISMB.
[81] Rakesh Agarwal,et al. Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.
[82] Katrien van Driessen,et al. A Fast Algorithm for the Minimum Covariance Determinant Estimator , 1999, Technometrics.
[83] Huan Liu,et al. Subspace clustering for high dimensional data: a review , 2004, SKDD.
[84] Hans-Peter Kriegel,et al. Detecting clusters in moderate-to-high dimensional data: subspace clustering, pattern-based clustering, and correlation clustering , 2008, Proc. VLDB Endow..
[85] Hans-Peter Kriegel,et al. Density-Connected Subspace Clustering for High-Dimensional Data , 2004, SDM.
[86] Thomas Yuster,et al. The Reduced Row Echelon Form of a Matrix Is Unique: A Simple Proof , 1984 .
[87] Christos Faloutsos,et al. Beyond uniformity and independence: analysis of R-trees using the concept of fractal dimension , 1994, PODS.
[88] A. Zimek,et al. Deriving quantitative models for correlation clusters , 2006, KDD '06.
[89] Bernhard Liebl,et al. Very high compliance in an expanded MS-MS-based newborn screening program despite written parental consent. , 2002, Preventive medicine.
[90] Arlindo L. Oliveira,et al. Biclustering algorithms for biological data analysis: a survey , 2004, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[91] Stefan Kramer,et al. Quantitative association rules based on half-spaces: an optimization approach , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).
[92] Lothar Thiele,et al. A systematic comparison and evaluation of biclustering methods for gene expression data , 2006, Bioinform..
[93] Jian Tang,et al. Enhancing Effectiveness of Outlier Detections for Low Density Patterns , 2002, PAKDD.
[94] Ping Chen,et al. Using the fractal dimension to cluster datasets , 2000, KDD '00.
[95] Raymond T. Ng,et al. Algorithms for Mining Distance-Based Outliers in Large Datasets , 1998, VLDB.
[96] Richard M. Karp,et al. Discovering local structure in gene expression data: the order-preserving submatrix problem , 2002, RECOMB '02.
[97] Elke Achtert,et al. Robust, Complete, and Efficient Correlation Clustering , 2007, SDM.
[98] Philip S. Yu,et al. Clustering through decision tree construction , 2000, CIKM '00.
[99] Michael K. Ng,et al. HARP: a practical projected clustering algorithm , 2004, IEEE Transactions on Knowledge and Data Engineering.
[100] Hans-Peter Kriegel,et al. The X-tree : An Index Structure for High-Dimensional Data , 2001, VLDB.
[101] Jon R. Kettenring,et al. A Perspective on Cluster Analysis , 2008, Stat. Anal. Data Min..
[102] Charu C. Aggarwal,et al. On the Surprising Behavior of Distance Metrics in High Dimensional Spaces , 2001, ICDT.
[103] W. R. Buckland,et al. Outliers in Statistical Data , 1979 .
[104] J. Hartigan. Direct Clustering of a Data Matrix , 1972 .
[105] Elke Achtert,et al. ELKI: A Software System for Evaluation of Subspace Clustering Algorithms , 2008, SSDBM.
[106] Jinyan Li,et al. Mining Maximal Quasi-Bicliques to Co-Cluster Stocks and Financial Ratios for Value Investment , 2006, Sixth International Conference on Data Mining (ICDM'06).
[107] Petra Perner,et al. Data Mining - Concepts and Techniques , 2002, Künstliche Intell..
[108] R. Haralick,et al. LINEAR MANIFOLD CORRELATION CLUSTERING , 2007 .
[109] Michael K. Ng,et al. On discovery of extremely low-dimensional clusters using semi-supervised projected clustering , 2005, 21st International Conference on Data Engineering (ICDE'05).
[110] Elke Achtert,et al. Finding Hierarchies of Subspace Clusters , 2006, PKDD.
[111] Christos Faloutsos,et al. On the 'Dimensionality Curse' and the 'Self-Similarity Blessing' , 2001, IEEE Trans. Knowl. Data Eng..
[112] Yi Zhang,et al. Entropy-based subspace clustering for mining numerical data , 1999, KDD '99.
[113] Bart De Moor,et al. Biclustering microarray data by Gibbs sampling , 2003, ECCB.
[114] Stefan Kramer,et al. Analyzing microarray data using quantitative association rules , 2005, ECCB/JBI.
[115] Roded Sharan,et al. Discovering statistically significant biclusters in gene expression data , 2002, ISMB.
[116] Martin Ester,et al. P3C: A Robust Projected Clustering Algorithm , 2006, Sixth International Conference on Data Mining (ICDM'06).
[117] Hans-Peter Kriegel,et al. Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering , 2009, TKDD.
[118] Anthony Wirth,et al. Correlation Clustering , 2010, Encyclopedia of Machine Learning and Data Mining.
[119] Anthony K. H. Tung,et al. Mining top-n local outliers in large databases , 2001, KDD '01.
[120] Stefan Berchtold,et al. Efficient Biased Sampling for Approximate Clustering and Outlier Detection in Large Data Sets , 2003, IEEE Trans. Knowl. Data Eng..
[121] Sharad Mehrotra,et al. Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces , 2000, VLDB.
[122] Aristides Gionis,et al. Dimension induced clustering , 2005, KDD '05.
[123] Xiaodi Huang,et al. A Fast Algorithm for Finding Correlation Clusters in Noise Data , 2007, PAKDD.
[124] Philip S. Yu,et al. Fast algorithms for projected clustering , 1999, SIGMOD '99.
[125] Christian Böhm,et al. Independent quantization: an index compression technique for high-dimensional data spaces , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).
[126] Michael Ruogu Zhang,et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.
[127] J. Friedman. Clustering objects on subsets of attributes , 2002 .
[128] Hans-Peter Kriegel,et al. The pyramid-technique: towards breaking the curse of dimensionality , 1998, SIGMOD '98.
[129] Richard O. Duda,et al. Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.
[130] Hans-Hermann Bock,et al. Two-mode clustering methods: astructuredoverview , 2004, Statistical methods in medical research.
[131] Elke Achtert,et al. Detection and Visualization of Subspace Cluster Hierarchies , 2007, DASFAA.
[132] Hans-Peter Kriegel,et al. A generic framework for efficient subspace clustering of high-dimensional data , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).
[133] G. Church,et al. Systematic determination of genetic network architecture , 1999, Nature Genetics.
[134] Hans-Peter Kriegel,et al. A distribution-based clustering algorithm for mining in large spatial databases , 1998, Proceedings 14th International Conference on Data Engineering.
[135] Christos Faloutsos,et al. On data mining, compression, and Kolmogorov complexity , 2007, Data Mining and Knowledge Discovery.
[136] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[137] Osmar R. Zaïane,et al. An Efficient Reference-Based Approach to Outlier Detection in Large Datasets , 2006, Sixth International Conference on Data Mining (ICDM'06).
[138] Daniel A. Keim,et al. An Efficient Approach to Clustering in Large Multimedia Databases with Noise , 1998, KDD.
[139] Theodore Johnson,et al. Fast Computation of 2-Dimensional Depth Contours , 1998, KDD.
[140] Ira Assent,et al. VISA: visual subspace clustering analysis , 2007, SKDD.
[141] Anthony K. H. Tung,et al. Ranking Outliers Using Symmetric Neighborhood Relationship , 2006, PAKDD.
[142] Hans-Peter Kriegel,et al. LOF: identifying density-based local outliers , 2000, SIGMOD '00.