In this text, an international team of 44 data mining experts specifically explore new methodologies or examine case studies in this new and multi-disciplinary topic.
Table of Contents
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Table of Contents
Data Mining—Opportunities and Challenges
Preface
Chapter I
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A Survey of Bayesian Data Mining
Chapter II
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Control of Inductive Bias in Supervised Learning Using Evolutionary Computation—A Wrapper-Based Approach
Chapter III
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Cooperative Learning and Virtual Reality-Based Visualization for Data Mining
Chapter IV
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Feature Selection in Data Mining
Chapter V
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Parallel and Distributed Data Mining through Parallel Skeletons and Distributed Objects
Chapter VI
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Data Mining Based on Rough Sets
Chapter VII
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The Impact of Missing Data on Data Mining
Chapter VIII
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Mining Text Documents for Thematic Hierarchies Using Self-Organizing Maps
Chapter IX
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The Pitfalls of Knowledge Discovery in Databases and Data Mining
Chapter X
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Maximum Performance Efficiency Approaches for Estimating Best Practice Costs
Chapter XI
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Bayesian Data Mining and Knowledge Discovery
Chapter XII
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Mining Free Text for Structure
Chapter XIII
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Query-By-Structure Approach for the Web
Chapter XIV
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Financial Benchmarking Using Self-Organizing Maps—Studying the International Pulp and Paper Industry
Chapter XV
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Data Mining in Health Care Applications
Chapter XVI
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Data Mining for Human Resource Information Systems
Chapter XVII
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Data Mining in Information Technology and Banking Performance
Chapter XVIII
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Social, Ethical and Legal Issues of Data Mining
Chapter XIX
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Data Mining in Designing an Agent-Based DSS
Chapter XX
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Critical and Future Trends in Data Mining—A Review of Key Data Mining Technologies/Applications
Table of Contents
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