Foundations of Data Mining and knowledge Discovery

Setsuo Ohsuga (auth.), Professor Tsau Young Lin, Professor Setsuo Ohsuga, Dr. Churn-Jung Liau, Professor Xiaohua Hu, Professor Shusaku Tsumoto (eds.)9783540262572, 3-540-26257-1, 3-540-25729-2, 3-540-26071-4

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.

Table of contents :
Knowledge Discovery as Translation….Pages 1-19
Mathematical Foundation of Association Rules – Mining Associations by Solving Integral Linear Inequalities….Pages 21-42
Comparative Study of Sequential Pattern Mining Models….Pages 43-70
Designing Robust Regression Models….Pages 71-86
A Probabilistic Logic-based Framework for Characterizing Knowledge Discovery in Databases….Pages 87-100
A Careful Look at the Use of Statistical Methodology in Data Mining….Pages 101-117
Justification and Hypothesis Selection in Data Mining….Pages 119-130
On Statistical Independence in a Contingency Table….Pages 131-141
A Comparative Investigation on Model Selection in Binary Factor Analysis….Pages 143-160
Extraction of Generalized Rules with Automated Attribute Abstraction….Pages 161-170
Decision Making Based on Hybrid of Multi-Knowledge and Naïve Bayes Classifier….Pages 171-184
First-Order Logic Based Formalism for Temporal Data Mining * ….Pages 185-210
An Alternative Approach to Mining Association Rules….Pages 211-231
Direct Mining of Rules from Data with Missing Values….Pages 233-264
Cluster Identification Using Maximum Configuration Entropy….Pages 265-276
Mining Small Objects in Large Images Using Neural Networks….Pages 277-303
Improved Knowledge Mining with the Multimethod Approach….Pages 305-318
Posting Act Tagging Using Transformation-Based Learning….Pages 319-331
Identification of Critical Values in Latent Semantic Indexing….Pages 333-346
Reporting Data Mining Results in a Natural Language….Pages 347-361
An Algorithm to Calculate the Expected Value of an Ongoing User Session….Pages 363-375