Data Mining: Multimedia, Soft Computing, and Bioinformatics

A primer on traditional hard and emerging soft computing approaches for mining multimedia data
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.
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:The role of soft computing and its principles in data miningPrinciples and classical algorithms on string matching and their role in data (mainly text) miningData compression principles for both lossless and lossy techniques, including their scope in data miningAccess of data using matching pursuits both in raw and compressed data domainsApplication in mining biological databases

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

Nota biograficzna:
SUSHMITA MITRA, PHD, is a Professor at Machine Intelligence Unit, Indian Statistical Institute, in Calcutta. She is a coauthor of Neuro–Fuzzy Pattern Recognition: Methods in Soft Computing, also published by Wiley.
TINKU ACHARYA, PHD, 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.

Okładka tylna:
A primer on traditional hard and emerging soft computing approaches for mining multimedia data
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.
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:The role of soft computing and its principles in data miningPrinciples and classical algorithms on string matching and their role in data (mainly text) miningData compression principles for both lossless and lossy techniques, including their scope in data miningAccess of data using matching pursuits both in raw and compressed data domainsApplication in mining biological databases