Grouping Multidimensional Data
Recent Advances in Clustering
1 edition
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Author
Contributions
- Jacob Kogan (Editor) - Contributor
- Charles Nicholas (Editor) - Contributor
- Marc Teboulle (Editor) - Contributor
Publication
2006-02-10 - Springer
Language
English
Word Count
67,000 words, Guess
Page Count
268 pages
Physical Format
Hardcover
Identifiers
- Open LibraryOL9055837M
- ISBN-139783540283485
- ISBN-10354028348X
- OCLC Control Number63196977
- OCLC Control Numbergroupingmultidim00koga
and 3 more
- Library of Congress Control Number2005933258
- LibraryThing4561241
- Goodreads1588354
Classifications
- LCCQA76.9.D35QA75.5-76.
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.
Subjects
Topics
Other Editions
- Grouping Multidimensional Data: Recent Advances in Clustering
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