- Full Description
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.
- Table of Contents
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).
- 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).
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