The Handbook of Data Mining

by Ye; Nong

• ISBN13: 9780805855630
• ISBN10: 0805855637
• Format: Nonspecific Binding
• Copyright: 2004-11-19
• Publisher: CRC Press

Summary

This book is the first comprehensive one to feature systematic coverage of the concepts, techniques, examples, issues, software tools, and future advancements of data mining. The demand for DM apps are increasing in industries, government, and academia.

Author Biography

Dr. Nong Ye is Director of the Information and Systems Assurance Laboratory, Professor of Industrial Engineering, and an Affiliated Professor of Computer Science and Engineering at Arizona State University. She received her Ph.D. in Industrial Engineering from Purdue University, and her M.S. and B.S. in Computer Science. Her research focuses on assuring process quality and reliability of information systems, manufacturing, and enterprise and human-machine systems, using data mining, statistics and optimization techniques. Dr. Ye is an Associate Editor of IEEE Transactions on Reliability and IEEE Transactions on Systems, Man, and Cybernetics. She is also an editorial board member of the International Journal of Human-Computer Interaction and theInternational Journal of Cognitive Ergonomics.

Table of Contents

Foreword	p. xviii
Preface	p. xix
About the Editor	p. xxiii
Contributors	p. xxvii
Methodologies of Data Mining	p. 1
Decision Trees	p. 3
References	p. 23
Association Rules	p. 25
References	p. 38
Artificial Neural Network Models for Data Mining	p. 41
References	p. 65
Statistical Analysis of Normal and Abnormal Data	p. 67
Bayesian Data Analysis	p. 103
References	p. 129
Hidden Markov Processes and Sequential Pattern Mining	p. 133
References	p. 155
Strategies and Methods for Prediction	p. 159
References	p. 190
Principal Components and Factor Analysis	p. 193
References	p. 212
Psychometric Methods of Latent Variable Modeling	p. 215
References	p. 244
Scalable Clustering	p. 247
References	p. 275
Time Series Similarity and Indexing	p. 279
References	p. 302
Nonlinear Time Series Analysis	p. 305
References	p. 338
Distributed Data Mining	p. 341
References	p. 358
Management of Data Mining	p. 363
Data Collection, Preparation, Quality, and Visualization	p. 365
Data Storage and Management	p. 393
Feature Extraction, Selection, and Construction	p. 409
References	p. 422
Performance Analysis and Evaluation	p. 425
References	p. 439
Security and Privacy	p. 441
References	p. 451
Emerging Standards and Interfaces	p. 453
Applications of Data Mining	p. 461
Mining Human Performance Data	p. 463
References	p. 477
Mining Text Data	p. 481
References	p. 517
Mining Geospatial Data	p. 519
References	p. 545
Mining Science and Engineering Data	p. 549
References	p. 569
Mining Data in Bioinformatics	p. 573
Mining Customer Relationship Management (crm) Data	p. 597
References	p. 616
Mining Computer and Network Security Data	p. 617
References	p. 635
Mining Image Data	p. 637
Mining Manufacturing Quality Data	p. 657
Author Index	p. 669
Subject Index	p. 681
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