Intelligent Data Analysis: Developing New Methodologies Through Pattern Discovery and Recovery

Intelligent Data Analysis: Developing New Methodologies Through Pattern Discovery and Recovery

Hsiao-Fan Wang
Indexed In: SCOPUS View 1 More Indices
Release Date: July, 2008|Copyright: © 2009 |Pages: 366
DOI: 10.4018/978-1-59904-982-3
ISBN13: 9781599049823|ISBN10: 1599049821|EISBN13: 9781599049830
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Description & Coverage
Description:

Pattern Recognition has a long history of applications to data analysis in business, military and social economic activities. While the aim of pattern recognition is to discover the pattern of a data set, the size of the data set is closely related to the methodology one adopts for analysis.

Intelligent Data Analysis: Developing New Methodologies Through Pattern Discovery and Recovery tackles those data sets and covers a variety of issues in relation to intelligent data analysis so that patterns from frequent or rare events in spatial or temporal spaces can be revealed. This book brings together current research, results, problems, and applications from both theoretical and practical approaches.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Atanassov's Intuitionistic Fuzzy Sets
  • Blackbox algorithms
  • Cumulative prospect theory
  • Data construction analysis
  • Ecological Data
  • Evolutionary Computing
  • Fuzzy neural network models
  • Fuzzy Sets
  • Gene Expression Data
  • Genetic learning
  • Golden eagle satellite data
  • Intelligent data analysis
  • Knowledge Discovery
  • Light scattering
  • Particle identification
  • Pattern discovery
  • Telecom industry
  • TREENET
  • Virtual sampling
Reviews & Statements

This book will be useful for both researchers and practitioners who are interested in receiving comprehensive views and insights from the variety of issues covered in relation to pattern discovery and recovery.

– Hsiao-Fan Wang, National Tsing Hua University, Taiwan, ROC

In these 28 articles, editor Wang and her contributors emphasize new discoveries that have widened methodological choices even further in the analysis of artificial data.

– Book News Inc. (March 2009)

Intended for the computer-science professional, each essay is rich with field-specific jargon, but the topics covered are also relevant to the concerns of e-commerce administrators, public interest groups, and civil policymakers.

– Library Journal (June 2009)
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Editor/Author Biographies
Hsiao-Fan Wang is the Tsing Hua Chair Professor and the Vice Dean of the College of Engineering of National Tsing Hua University (Taiwan). She has been teaching at the Department of Industrial Engineering and Engineering Management at the same university, NTHU after she graduated from Cambridge University (UK) in 1981. She used to be the Head of the Department of IEEM, NTHU, President of Chinese Fuzzy Systems Association, Vice President of International Fuzzy Systems Association and Erskine Fellow of Canterbury University, NZ. Also, she has been awarded the Distinguished Research Award from National Science Council of Taiwan (ROC); Distinguished Contracted Research Fellow of NSC and Distinguished Teaching Award of Engineering College, NTHU. She used to be the editor-in-chief of the Journal of Chinese Industrial Engineering Association; also the Journal of Chinese Fuzzy Set and Theories and now is the area editor of several international journals. Her research interests are in multicriteria decision making, fuzzy set theory and operations research.
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