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Introduction to clustering large and high-dimensional data

Author: Jacob Kogan
Publisher: Cambridge ; New York : Cambridge University Press, 2007.
Edition/Format:   Print book : EnglishView all editions and formats
Database:WorldCat
Summary:
"This book focuses on a few of the most important clustering algorithms, providing a detailed account of these major models in an information retrieval context. The beginning chapters introduce the classic algorithms in detail, while the later chapters describe clustering through divergences and show recent research for more advanced audiences."--Jacket.
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Details

Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Jacob Kogan
ISBN: 0521852676 9780521852678 0521617936 9780521617932
OCLC Number: 70707967
Description: xvi, 205 pages : illustrations ; 24 cm
Contents: Introduction to motivation --
Quadratic k-means algorithm --
BIRCH --
Spherical k-means algorithm --
Linear algebra techniques --
Information theoretic clustering --
Clustering with optimization techniques --
k-means clustering divergences --
Assessment of clustering results --
Optimization and linear algebra background --
Solutions to selected problems.
Responsibility: Jacob Kogan.

Abstract:

Focuses on a few of the important clustering algorithms in the context of information retrieval.  Read more...
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"...this book may serve as a useful reference for scientists and engineers who need to understand the concepts of clustering in general and/or to focus on text mining applications. It is also Read more...

 
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