Foundations of Data Mining and Knowledge Discovery

Tsau Young Lin9783540262572, 3-540-26257-1

“Foundations of Data Mining and Knowledge Discovery” contains the latest results and new directions in data mining research. Data mining, which integrates various technologies, including computational intelligence, database and knowledge management, machine learning, soft computing, and statistics, is one of the fastest growing fields in computer science. Although many data mining techniques have been developed, further development of the field requires a close examination of its foundations. This volume presents the results of investigations into the foundations of the discipline, and represents the state of the art for much of the current research. This book will prove extremely valuable and fruitful for data mining researchers, no matter whether they would like to uncover the fundamental principles behind data mining, or apply the theories to practical applications.

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Table of contents :
Foundations of Data Mining and Knowledge Discovery……Page 1
Preface……Page 6
Contents……Page 12
Part I Foundations of Data Mining……Page 15
Knowledge Discovery as Translation……Page 17
Mathematical Foundation of Association Rules – Mining Associations by Solving Integral Linear Inequalities……Page 34
Comparative Study of Sequential Pattern Mining Models……Page 56
Designing Robust Regression Models……Page 84
A Probabilistic Logic-based Framework for Characterizing Knowledge Discovery in Databases……Page 100
A Careful Look at the Use of Statistical Methodology in Data Mining……Page 114
Justification and Hypothesis Selection in Data Mining……Page 131
On Statistical Independence in a Contingency Table……Page 143
Part II Methods of Data Mining……Page 154
A Comparative Investigation on Model Selection in Binary Factor Analysis……Page 156
Extraction of Generalized Rules with Automated Attribute Abstraction……Page 172
Decision Making Based on Hybrid
of Multi-Knowledge and Naive Bayes Classifier……Page 182
First-Order Logic Based Formalism for Temporal Data Mining……Page 196
An Alternative Approach to Mining Association Rules……Page 222
Direct Mining of Rules from Data with Missing Values……Page 243
Cluster Identification Using Maximum Configuration Entropy……Page 275
Mining Small Objects in Large Images Using Neural Networks……Page 287
Improved Knowledge Mining with the Multimethod Approach……Page 314
Part III General Knowledge Discovery……Page 328
Posting Act Tagging Using Transformation-Based Learning……Page 330
Identification of Critical Values in Latent Semantic Indexing……Page 341
Reporting Data Mining Results in a Natural Language……Page 355
An Algorithm to Calculate the Expected Value of an Ongoing User Session……Page 370