Grouping Multidimensional Data: Recent Advances in Clustering by Jacob KoganGrouping Multidimensional Data: Recent Advances in Clustering by Jacob Kogan

Grouping Multidimensional Data: Recent Advances in Clustering

byJacob KoganEditorCharles Nicholas, Marc Teboulle

Paperback | February 12, 2010

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Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anomaly detection.Kogan and his co-editors have put together recent advances in clustering large and high-dimension data. Their volume addresses new topics and methods which are central to modern data analysis, with particular emphasis on linear algebra tools, opimization methods and statistical techniques. The contributions, written by leading researchers from both academia and industry, cover theoretical basics as well as application and evaluation of algorithms, and thus provide an excellent state-of-the-art overview.The level of detail, the breadth of coverage, and the comprehensive bibliography make this book a perfect fit for researchers and graduate students in data mining and in many other important related application areas.
Jacob Kogan is an Associate Professor in the Department of Mathematics and Statistics at the University of Maryland Baltimore County. Dr. Kogan received his Ph.D. in Mathematics from Weizmann Institute of Science, and has held teaching and research positions at the University of Toronto and Purdue University. His research interests inc...
Title:Grouping Multidimensional Data: Recent Advances in ClusteringFormat:PaperbackDimensions:268 pagesPublished:February 12, 2010Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3642066542

ISBN - 13:9783642066542


Table of Contents

Foreword (J. Han).- The Star Clustering Algorithm for Information Organization (J. A. Aslam, E. Pelekhov, D. Rus).- A Survey of Clustering Data Mining Techniques (P. Berkhin).- Similarity-based Text Clustering: A Comparitive Study (J. Ghosh, A. Strehl).- Clustering Very Large Data Sets with Principal Direction Divisive Partitioning (D. Littau, D. Boley).- Clustering with Entropy-like k-means ALgorithms (M. Teboulle, P. Berkhin, I. Dhillon, Y. Guan, J. Kogan).- Sampling Methods for Building Initial Partitions (Z. Volkovich, J. Kogan, Ch. Nicholas).- TMG: A MATLAB Toolbox for Generating Term-Document Matrices from Text Collections (D. Zeimpekis, E. Gallopoulos).- Criterion Functions for Clustering on High Dimensional Data (Y. Zhao, G. Karypis).- Index.