title: Understanding Complex Datasets: Data Mining with Matrix Decompositions (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
author: David Skillicorn
description: <P> One of this books 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.<BR><EM>Technometrics</EM>, February 2009, Vol. 51, No. 1</P>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.<BR>Michael W. Berry, University of Tennessee, Knoxville, USA<BR><BR>[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.<BR>Bruce Hendrickson, Sandia National Laboratories, Albuquerque, New Mexico, USA
zlib_text: Data Mining with R
  Learning with Case Studies
  
  Chapman & Hall/CRC 
  Data Mining and Knowledge Discovery Series
  UNDERSTANDING COMPLEX DATASETS: 
  DATA MINING WITH MATRIX DECOMPOSITIONS
  David Skillicorn
  COMPUTATIONAL METHODS OF FEATURE 
  SELECTION 
  Huan Liu and Hiroshi Motoda
  CONSTRAINED CLUSTERING: ADVANCES IN 
  ALGORITHMS, THEORY, AND APPLICATIONS
  Sugato Basu, Ian Davidson, and Kiri L. Wagstaff
  KNOWLEDGE DISCOVERY FOR 
  COUNTERTERRORISM AND LAW ENFORCEMENT 
  David Skillicorn
  MULTIMEDIA DATA MINING: A SYSTEMATIC 
  INTRODUCTION TO CONCEPTS AND THEORY
  Zhongfei Zhang and Ruofei Zhang
  NEXT GENERATION OF DATA MINING 
  Hillol Kargupta, Jiawei Han, Philip S. Yu,  
  Rajeev Motwani, and Vipin Kumar
  DATA MINING FOR DESIGN AND MARKETING 
  Yukio Ohsawa and Katsutoshi Yada 
  THE TOP TEN ALGORITHMS IN DATA MINING 
  Xindong Wu and Vipin Kumar
  GEOGRAPHIC DATA MINING AND  
  KNOWLEDGE DISCOVERY, SECOND EDITION
  Harvey J. Miller and Jiawei Han
  TEXT MINING: CLASSIFICATION, CLUSTERING, 
  AND APPLICATIONS 
  Ashok N. Srivast