Features
- Cover Type: Hard Cover with 260 pages
- Published by: Chapman & Hall/CRC
- Edition: 1st Edition May 17, 2007
- Written in: English
- ISBN 10 Number: 1584888326
- ISBN 13 Number: 978-1584888321
-
Book Dimensions:
9.4 x 6.1 x 0.7 inches
- Weighs: 1.1 pounds
Product Review
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 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
[This book] is suffused with insightful suggestions for analytical methods and interpretations, drawn from the authors 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
Product Description
Making unusual 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.
Reader ReviewsIf you have a background in mathematics, but never forayed into Data Mining, read this on a plane from New York to Chicago.You will love it. A excellent entry point, should I say
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