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Preface. <p>1. Introduction to Data Mining.</p> <p>2. Soft Computing.</p> <p>3. Multimedia Data Compression.</p> <p>4. String Matching.</p> <p>5. Classification in Data Mining.</p> <p>6. Clustering in Data Mining.</p> <p>7. Association Rules.</p> <p>8. Rule Mining with Soft Computing.</p> <p>9. Multimedia Data Mining.</p> <p>10. Bioinformatics: An Application.</p> <p>Index.</p> <p>About the Authors.</p>

"…an excellent primer on the subject of data mining with an accessible introduction to the fundamental and advanced data mining technologies." (<i>Journal of Electronic Imaging</i>, January-March 2006) <p>"Applied statisticians and probabilists will like this book very much." (<i>Journal of Statistical Computation and Simulation</i>, November 2005)</p> <p>"…the book is an impressive and broad overview...a general roadmap of what methods are available and where to look." (<i>Journal of Intelligent & Fuzzy Systems</i>, Vol. 16, No. 2, 2005)</p> <p>"This readable survey describes multimedia, soft computing, and bioinformatics strategies for a number of data types…" (<i>Business Horizons</i>, September- October 2004)</p> <p>"…an accessible introduction to fundamental and advanced data mining technologies. It will be an excellent book for both beginners and professionals." (<i>Computing Reviews.com</i>, April 20, 2004)</p> <p>"Overall, this is a nice, easy-to-read book for those already working in the area of data mining." (<i>Technometrics</i>, August 2004, Vol. 46, No. 3)</p>

<b>SUSHMITA MITRA, PHD,</b> is a Professor at Machine Intelligence Unit, Indian Statistical Institute, in Calcutta. She is a coauthor of <i>Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing</i>, also published by Wiley. <p><b>TINKU ACHARYA, PHD,</b> Senior Executive vice president and Chief Science Officer of Avisere Inc., Tucson, Arizona, is involved in multimedia data mining applications. He is also an adjunct professor in the Department of Electrical Engineering at Arizona State University. He was recognized as the Most Prolific Inventor of Intel Corporation Worldwide in 1999.</p>

A primer on traditional hard and emerging soft computing approaches for mining multimedia data <p>While the digital revolution has made huge volumes of high dimensional multimedia data available, it has also challenged users to extract the information they seek from heretofore unthinkably huge datasets. Traditional hard computing data mining techniques have concentrated on flat-file applications. Soft computing tools–such as fuzzy sets, artificial neural networks, genetic algorithms, and rough sets–however, offer the opportunity to apply a wide range of data types to a variety of vital functions by handling real-life uncertainty with low-cost solutions. Data Mining: Multimedia, Soft Computing, and Bioinformatics provides an accessible introduction to fundamental and advanced data mining technologies.</p> <p>This readable survey describes data mining strategies for a slew of data types, including numeric and alpha-numeric formats, text, images, video, graphics, and the mixed representations therein. Along with traditional concepts and functions of data mining–like classification, clustering, and rule mining–the authors highlight topical issues in multimedia applications and bioinformatics. Principal topics discussed throughout the text include:</p> <ul> <li>The role of soft computing and its principles in data mining</li> <li>Principles and classical algorithms on string matching and their role in data (mainly text) mining</li> <li>Data compression principles for both lossless and lossy techniques, including their scope in data mining</li> <li>Access of data using matching pursuits both in raw and compressed data domains</li> <li>Application in mining biological databases</li> </ul>

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