Grouping Multidimensional Data : Recent Advances in Clustering /
Contributor(s): Kogan, Jacob [editor.] | Nicholas, Charles [editor.] | Teboulle, Marc [editor.] | SpringerLink (Online service).
Material type:
BookPublisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006.Description: XII, 268 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540283492.Subject(s): Computer science | Data structures (Computer science) | Computer science -- Mathematics | Information storage and retrieval | Pattern recognition | Statistics | Computer Science | Data Structures, Cryptology and Information Theory | Information Storage and Retrieval | Statistical Theory and Methods | Math Applications in Computer Science | Statistics and Computing/Statistics Programs | Pattern RecognitionDDC classification: 005.74 Online resources: Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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E books
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PK Kelkar Library, IIT Kanpur | Available | EBK402 |
The Star Clustering Algorithm for Information Organization -- A Survey of Clustering Data Mining Techniques -- Similarity-Based Text Clustering: A Comparative Study -- Clustering Very Large Data Sets with Principal Direction Divisive Partitioning -- Clustering with Entropy-Like k-Means Algorithms -- Sampling Methods for Building Initial Partitions -- TMG: A MATLAB Toolbox for Generating Term-Document Matrices from Text Collections -- Criterion Functions for Clustering on High-Dimensional Data.
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.
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