Understanding Complex Datasets: Data Mining with Matrix Decompositions (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) [Hardcover]

David Skillicorn (Author)

Book Description

Publication Date: May 17, 2007 | ISBN-10: 1584888326 | ISBN-13: 978-1584888321 | Edition: 1

Making obscure knowledge about matrix decompositions widely available, Understanding Complex Datasets: Data Mining with Matrix Decompositions discusses the most common matrix decompositions and shows how they can be used to analyze large datasets in a broad range of application areas. Without having to understand every mathematical detail, the book helps you determine which matrix is appropriate for your dataset and what the results mean.

Explaining the effectiveness of matrices as data analysis tools, the book illustrates the ability of matrix decompositions to provide more powerful analyses and to produce cleaner data than more mainstream techniques. The author explores the deep connections between matrix decompositions and structures within graphs, relating the PageRank algorithm of Google's search engine to singular value decomposition. He also covers dimensionality reduction, collaborative filtering, clustering, and spectral analysis. With numerous figures and examples, the book shows how matrix decompositions can be used to find documents on the Internet, look for deeply buried mineral deposits without drilling, explore the structure of proteins, detect suspicious emails or cell phone calls, and more.

Concentrating on data mining mechanics and applications, this resource helps you model large, complex datasets and investigate connections between standard data mining techniques and matrix decompositions.

Editorial Reviews

Review

… One of this book’s attractive features is that every chapter contains a discussion relating to the algorithmic issues. One scenario is used as a running illustrative example throughout the book. Several other examples are discussed in different chapters. These examples should help the reader understand the advantages as well as the practical problems associated with any of the proposed matrix-based data mining techniques covered in the book. I recommend this book for anyone interested in using matrix methods for data mining.
—Technometrics, February 2009, Vol. 51, No. 1

This could be a nice companion book for courses in data mining or applied linear algebra. Producing a clear taxonomy of the use and intentions of matrix decompositions in data analysis is very useful to both students and researchers. … Those working with large-scale complex datasets will definitely find this work useful. … I would definitely use it in my own course in data mining.
—Michael W. Berry, University of Tennessee, Knoxville, USA

[This book] is suffused with insightful suggestions for analytical methods and interpretations, drawn from the author's own research and his reading of the literature. …The book has two great strengths. The first is its attempt to provide a unifying framework from which to view a host of important analytical methodologies based on matrix methods. … Second, the book is extremely strong on interpreting the results of matrix methods. … [It] assembles and explains a diverse set of insights that are otherwise widely scattered in the literature. This alone makes the book an important contribution to the community.
—Bruce Hendrickson, Sandia National Laboratories, Albuquerque, New Mexico, USA

About the Author

Queen's University, Kingston, Ontario, Canada

Product Details

• Hardcover: 260 pages
• Publisher: Chapman and Hall/CRC; 1 edition (May 17, 2007)
• Language: English
• ISBN-10: 1584888326
• ISBN-13: 978-1584888321
• Product Dimensions: 9.3 x 6.6 x 0.8 inches
• Shipping Weight: 1.1 pounds (View shipping rates and policies)
• Average Customer Review: 5.0 out of 5 stars  See all reviews (2 customer reviews)
• Amazon Best Sellers Rank: #504,823 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

11 of 12 people found the following review helpful:

5.0 out of 5 stars Comprehensive and well-written!, November 26, 2008

By 

Michael Barnathan - See all my reviews
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This review is from: Understanding Complex Datasets: Data Mining with Matrix Decompositions (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) (Hardcover)

I am doing research in data mining using tensor decompositions, and was very impressed by this book. It not only covers all of the important matrix and tensor decompositions, but also methods for computing them (with Matlab code!), the computational complexity of these methods, the tradeoffs and scenarios associated with each decomposition, and potential areas of application. To top it all off, the book is written in a highly approachable manner, covering the general concept of a decomposition and gradually discussing the specific decompositions from simplest to most complex. Highly recommended.

3 of 3 people found the following review helpful:

5.0 out of 5 stars Solid, in-depth coverage of topics whose importance grows daily, May 15, 2011

By 

Dan Brickley - See all my reviews

This review is from: Understanding Complex Datasets: Data Mining with Matrix Decompositions (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) (Hardcover)

Clearly written and accessible to those with fading math skills, this book goes into useful detail, background and context that most online treatments skip past.