Grouping Multidimensional Data

Recent Advances in Clustering

By: Jacob Kogan (Editor), Charles Nicholas (Editor), Marc Teboulle (Editor)

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Hardcover | 16 December 2005

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Hardcover
284 Pages

Dimensions(cm)
23.5 x 15.88 x 1.91

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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.

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You Can Find This Book In

Non-Fiction Computing & I.T.Databases Data Mining Mathematics Probability & Statistics Computer Science Artificial Intelligence Computer Vision Computer Programming & Software Development Algorithms & Data Structures

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Paperback

Published: 12th February 2010

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The Star Clustering Algorithm for Information Organization	p. 1
A Survey of Clustering Data Mining Techniques	p. 25
Similarity-Based Text Clustering: A Comparative Study	p. 73
Clustering Very Large Data Sets with Principal Direction Divisive Partitioning	p. 99
Clustering with Entropy-Like k-Means Algorithms	p. 127
Sampling Methods for Building Initial Partitions	p. 161
TMG: A MATLAB Toolbox for Generating Term-Document Matrices from Text Collections	p. 187
Criterion Functions for Clustering on High-Dimensional Data	p. 211
References	p. 239
Index	p. 265
Table of Contents provided by Ingram. All Rights Reserved.

Grouping Multidimensional Data

Recent Advances in Clustering

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

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