About this book
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.
Editors and affiliations
- Book Title Grouping Multidimensional Data
- Book Subtitle Recent Advances in Clustering
- DOI https://doi.org/10.1007/3-540-28349-8
- Copyright Information Springer-Verlag Berlin Heidelberg 2006
- Publisher Name Springer, Berlin, Heidelberg
- eBook Packages Computer Science Computer Science (R0)
- Hardcover ISBN 978-3-540-28348-5
- Softcover ISBN 978-3-642-06654-2
- eBook ISBN 978-3-540-28349-2
- Edition Number 1
- Number of Pages XII, 268
- Number of Illustrations 0 b/w illustrations, 0 illustrations in colour
Data Structures and Information Theory
Information Storage and Retrieval
Statistical Theory and Methods
Math Applications in Computer Science
Statistics and Computing/Statistics Programs
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