Lin Ohsuga Liau – Foundations of Data Mining & Knowledge Discovery

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Foundations of Data Mining and Knowledge Discovery

Editors: Tsau Young Lin,  Setsuo Ohsuga,  Churn-Jung Liau, Xiaohua Hu, Shusaku Tsumoto
Collection of expanded versions of selected papers originally presented at the IEEE ICDM 2002 workshop on the Foundation of Data Mining and Discovery

Table of contents (21 chapters)

Front Matter

Knowledge Discovery as Translation
Setsuo Ohsuga

Pages 1-19
Mathematical Foundation of Association Rules – Mining Associations by Solving Integral Linear Inequalities
T.Y. Lin
Pages 21-42

Comparative Study of Sequential Pattern Mining Models
Hye-Chung (Monica) Kum, Susan Paulsen, Wei Wang
Pages 43-70

Designing Robust Regression Models
Murlikrishna Viswanathan, Kotagiri Ramamohanarao
Pages 71-86

A Probabilistic Logic-based Framework for Characterizing Knowledge Discovery in Databases
Ying Xie, Vijay V. Raghavan
Pages 87-100

A Careful Look at the Use of Statistical Methodology in Data Mining
Norman Matloff
Pages 101-117

Justification and Hypothesis Selection in Data Mining
Tuan-Fang Fan, Duen-Ren Liu, Churn-Jung Liau
Pages 119-130

On Statistical Independence in a Contingency Table
Shusaku Tsumoto
Pages 131-141

A Comparative Investigation on Model Selection in Binary Factor Analysis
Yujia An, Xuelei Hu, Lei Xu
Pages 143-160

Extraction of Generalized Rules with Automated Attribute Abstraction
Yohji Shidara, Mineichi Kudo, Atsuyoshi Nakamura
Pages 161-170

Decision Making Based on Hybrid of Multi-Knowledge and Naïve Bayes Classifier
QingXiang Wu, David Bell, Martin McGinnity, Gongde Guo
Pages 171-184

First-Order Logic Based Formalism for Temporal Data Mining*
Paul Cotofrei, Kilian Stoffel
Pages 185-210

An Alternative Approach to Mining Association Rules
Jan Rauch, Milan Šimůnek
Pages 211-231

Direct Mining of Rules from Data with Missing Values
Vladimir Gorodetsky, Oleg Karsaev, Vladimir Samoilov
Pages 233-264

Cluster Identification Using Maximum Configuration Entropy
C.H. Li
Pages 265-276

Mining Small Objects in Large Images Using Neural Networks
Mengjie Zhang
Pages 277-303

Improved Knowledge Mining with the Multimethod Approach
Mitja Lenič, Peter Kokol, Milan Zorman, Petra Povalej, Bruno Stiglic, Ryuichi Yamamoto
Pages 305-318

Posting Act Tagging Using Transformation-Based Learning
Tianhao Wu, Faisal M. Khan, Todd A. Fisher, Lori A. Shuler, William M. Pottenger
Pages 319-331

Identification of Critical Values in Latent Semantic Indexing
April Kontostathis, William M. Pottenger, Brian D. Davison
Pages 333-346

Reporting Data Mining Results in a Natural Language
Petr Strossa, Zdeněk Černý, Jan Rauch
Pages 347-361

An Algorithm to Calculate the Expected Value of an Ongoing User Session
S. Millán, E. Menasalvas, M. Hadjimichael, E. Hochsztain
Pages 363-375

About this book
“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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