Clustering Categorical Data Using Data Summaries and Spectral Techniques

[1]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[2]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[3]  Jon M. Kleinberg,et al.  Clustering categorical data: an approach based on dynamical systems , 2000, The VLDB Journal.

[4]  Robert E. Tarjan,et al.  Graph Clustering and Minimum Cut Trees , 2004, Internet Math..

[5]  S. Esakkirajan,et al.  Fundamentals of relational database management systems , 2007 .

[6]  William M. Pottenger,et al.  A framework for understanding Latent Semantic Indexing (LSI) performance , 2006, Inf. Process. Manag..

[7]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[8]  Shehroz S. Khan,et al.  Computation of Initial Modes for K-modes Clustering Algorithm Using Evidence Accumulation , 2007, IJCAI.

[9]  James J. Nolan,et al.  Methods for Understanding and Analyzing NIBRS Data , 1999 .

[10]  Michael Holzrichter,et al.  A Graph Based Method for Generating the Fiedler Vector of Irregular Problems , 1999, IPPS/SPDP Workshops.

[11]  Jan L. Harrington.  Relational Database Design Clearly Explained , 1998 .

[12]  Susan T. Dumais,et al.  Using Linear Algebra for Intelligent Information Retrieval , 1995, SIAM Rev..

[13]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[14]  C. Bron,et al.  Algorithm 457: finding all cliques of an undirected graph , 1973 .

[15]  Andrew B. Kahng,et al.  New spectral methods for ratio cut partitioning and clustering , 1991, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[16]  Johannes Gehrke,et al.  CACTUS—clustering categorical data using summaries , 1999, KDD '99.

[17]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[18]  R. Balakrishnan,et al.  A textbook of graph theory , 1999 .

[19]  Santosh S. Vempala,et al.  On clusterings: Good, bad and spectral , 2004, JACM.

[20]  Heikki Mannila,et al.  Principles of Data Mining , 2001, Undergraduate Topics in Computer Science.

[21]  J. F. Marcotorchino,et al.  Optimisation en analyse ordinale des données , 1979 .

[22]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[23]  Yi Li,et al.  COOLCAT: an entropy-based algorithm for categorical clustering , 2002, CIKM '02.

[24]  Naftali Tishby,et al.  Agglomerative Information Bottleneck , 1999, NIPS.

[25]  Renée J. Miller,et al.  LIMBO: Scalable Clustering of Categorical Data , 2004, EDBT.

[26]  Lior Rokach,et al.  Data Mining And Knowledge Discovery Handbook , 2005 .

[27]  Alan M. Frieze,et al.  Clustering Large Graphs via the Singular Value Decomposition , 2004, Machine Learning.

[28]  Naftali Tishby,et al.  The information bottleneck method , 2000, ArXiv.

[29]  Robert Tibshirani,et al.  Estimating the number of clusters in a data set via the gap statistic , 2000 .

[30]  Chris H. Q. Ding,et al.  K-means clustering via principal component analysis , 2004, ICML.

[31]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[32]  Shokri Z. Selim,et al.  K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Dorothea Wagner,et al.  Between Min Cut and Graph Bisection , 1993, MFCS.

[34]  Mehmed Kantardzic,et al.  Data Mining: Concepts, Models, Methods, and Algorithms , 2002 .

[35]  Joshua Zhexue Huang,et al.  Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values , 1998, Data Mining and Knowledge Discovery.

[36]  Adrian E. Raftery,et al.  How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis , 1998, Comput. J..

[37]  Sam T. Roweis,et al.  EM Algorithms for PCA and SPCA , 1997, NIPS.

[38]  Ravi B. Boppana,et al.  Eigenvalues and graph bisection: An average-case analysis , 1987, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987).

[39]  J. Moon,et al.  On cliques in graphs , 1965 .

[40]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[41]  C. Eckart,et al.  The approximation of one matrix by another of lower rank , 1936 .

[42]  Eman Abdu,et al.  A spectral-based clustering algorithm for categorical data using data summaries , 2009, DMMT '09.

[43]  David S. Johnson,et al.  Some Simplified NP-Complete Graph Problems , 1976, Theor. Comput. Sci..

[44]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[45]  William M. Pottenger,et al.  Detecting Patterns in the LSI Term-Term Matrix , 2002 .

[46]  Akira Tanaka,et al.  The worst-case time complexity for generating all maximal cliques and computational experiments , 2006, Theor. Comput. Sci..

[47]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[48]  I. Jolliffe.  Principal Component Analysis , 2002 .

[49]  Ohn Mar San,et al.  An alternative extension of the k-means algorithm for clustering categorical data , 2004 .

[50]  Golub Gene H. Et.Al.  Matrix Computations, 3rd Edition , 2007 .

[51]  Alex Pothen,et al.  PARTITIONING SPARSE MATRICES WITH EIGENVECTORS OF GRAPHS* , 1990 .

[52]  Sudipto Guha,et al.  ROCK: a robust clustering algorithm for categorical attributes , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[53]  Michael I. Jordan,et al.  On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.

[54]  Ira Assent,et al.  Clicks: An effective algorithm for mining subspace clusters in categorical datasets , 2007, Data Knowl. Eng..

[55]  Shai Ben-David,et al.  A Sober Look at Clustering Stability , 2006, COLT.

[56]  Inderjit S. Dhillon,et al.  Co-clustering documents and words using bipartite spectral graph partitioning , 2001, KDD '01.

[57]  Gang Wang,et al.  Crime data mining: a general framework and some examples , 2004, Computer.

[58]  Chris H. Q. Ding,et al.  Spectral Relaxation for K-means Clustering , 2001, NIPS.

[59]  H. Luetkepohl.  The Handbook of Matrices , 1996 .

[60]  Renée J. Miller,et al.  Scalable clustering of categorical data and applications , 2004 .

[61]  Howard N. Snyder.  The Overrepresentation of Juvenile Crime Proportions in Robbery Clearance Statistics , 1999 .

[62]  H. Ralambondrainy,et al.  A conceptual version of the K-means algorithm , 1995, Pattern Recognit. Lett..

[63]  J. Bezdek,et al.  c-means clustering with the l/sub l/ and l/sub infinity / norms , 1991 .

[64]  T. Landauer,et al.  Indexing by Latent Semantic Analysis , 1990 .