Advances in Knowledge Discovery and Data Mining

by Fayyad, Usama M.; Piatetsky-Shapiro, Gregory; Smyth, Padhraic; Uthurusamy, Ramasamy

• ISBN13: 9780262560979
• ISBN10: 0262560976
• Format: Paperback
• Copyright: 1996-02-01
• Publisher: Aaai Pr

Summary

Advances in Knowledge Discovery and Data Miningbrings together the latest research-in statistics, databases, machine learning, and artificial intelligence -that are part of the exciting and rapidly growing field of Knowledge Discovery and Data Mining. Topics covered include fundamental issues, classification and clustering, trend and deviation analysis, dependency modeling, integrated discovery systems, next generation database systems, and application case studies. The contributors include leading researchers and practitioners from academia, government laboratories, and private industry. The last decade has seen an explosive growth in the generation and collection of data. Advances in data collection, widespread use of bar codes for most commercial products, and the computerization of many business and government transactions have flooded us with data and generated an urgent need for new techniques and tools that can intelligently and automatically assist in transforming this data into useful knowledge. This book is a timely and comprehensive overview of the new generation of techniques and tools for knowledge discovery in data. Distributed for AAAI Press

Table of Contents

Foreword
Preface
From Data Mining to Knowledge Discovery: An Overview	p. 1
The Process of Knowledge Discovery in Databases: A Human-Centered Approach	p. 37
Graphical Models for Discovering Knowledge	p. 59
A Statistical Perspective on Knowledge Discovery in Databases	p. 83
Inductive Logic Programming and Knowledge Discovery in Databases	p. 117
Bayesian Classification (AutoClass): Theory and Results	p. 153
Discovering Informative Patterns and Data Cleaning	p. 181
Transforming Rules and Trees into Comprehensible Knowledge Structures	p. 205
Finding Patterns in Time Series: A Dynamic Programming Approach	p. 229
Explora: A Multipattern and Multistrategy Discovery Assistant	p. 249
Bayesian Networks for Knowledge Discovery	p. 273
Fast Discovery of Association Rules	p. 307
From Contingency Tables to Various Forms of Knowledge in Databases	p. 329
Integrating Inductive and Deductive Reasoning for Data Mining	p. 353
Metaqueries for Data Mining	p. 375
Exploration of the Power of Attribute-Oriented Induction in Data Mining	p. 399
Using Inductive Learning To Generate Rules for Semantic Query Optimization	p. 425
Data Surveyor: Searching the Nuggets in Parallel	p. 447
Automating the Analysis and Cataloging of Sky Surveys	p. 471
Selecting and Reporting What is Interesting: The KEFIR Application to Healthcare Data	p. 495
Modeling Subjective Uncertainty in Image Annotation	p. 517
Predicting Equity Returns from Securities Data with Minimal Rule Generation	p. 541
From Data Mining to Knowledge Discovery: Current Challenges and Future Directions	p. 561
App. A Knowledge Discovery in Databases Terminology	p. 573
App. B Data Mining and Knowledge Discovery Internet Resources	p. 593
About The Editors	p. 597
Index	p. 601
Table of Contents provided by Blackwell. All Rights Reserved.