Knowledge Discovery and Data Mining. Current Issues and New Applications

1. Front Matter

   Pages I-XIV

   PDF

2. Keynote Speeches and Invited Talk

   1. Perspective on Data Mining from Statistical Viewpoints

      • Yoshiharu Sato

      Pages 1-1

   2. Inductive Databases and Knowledge Scouts

      • Ryszard S. Michalski

      Pages 2-3

   3. Hyperlink-Aware Mining and Analysis of the Web

      • Andrew Tomkins

      Pages 4-4

3. Data Mining Theory

   1. Polynomial Time Matching Algorithms for Tree-Like Structured Patterns in Knowledge Discovery

      • Tetsuhiro Miyahara, Takayoshi Shoudai, Tomoyuki Uchida, Kenichi Takahashi, Hiroaki Ueda

      Pages 5-16

   2. Fast Discovery of Interesting Rules

      • Nobuhiro Yugami, Yuiko Ohta, Seishi Okamoto

      Pages 17-28

   3. Performance Controlled Data Reduction for Knowledge Discovery in Distributed Databases

      • Slobodan Vucetic, Zoran Obradovic

      Pages 29-39

   4. Minimum Message Length Criterion for Second-Order Polynomial Model Discovery

      • Grace W Rumantir

      Pages 40-48

   5. Frequent Itemset Counting Across Multiple Tables

      • Viviane Crestana Jensen, Nandit Soparkar

      Pages 49-61

   6. Frequent Closures as a Concise Representation for Binary Data Mining

      • Jean-François Boulicaut, Artur Bykowski

      Pages 62-73

   7. An Optimization Problem in Data Cube System Design

      • Edward Hung, David W. Cheung, Ben Kao, Yilong Liang

      Pages 74-85

   8. Exception Rule Mining with a Relative Interestingness Measure

      • Farhad Hussain, Huan Liu, Einoshin Suzuki, Hongjun Lu

      Pages 86-97

4. Feature Selection and Transformation

   1. Consistency Based Feature Selection

      • Manoranjan Dash, Huan Liu, Hiroshi Motoda

      Pages 98-109

   2. Feature Selection for Clustering

      • Manoranjan Dash, Huan Liu

      Pages 110-121

   3. A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases

      • Eamonn J. Keogh, Michael J. Pazzani

      Pages 122-133

   4. Missing Value Estimation Based on Dynamic Attribute Selection

      • K. C. Lee, J. S. Park, Y. S. Kim, Y. T. Byun

      Pages 134-137

   5. On Association, Similarity and Dependency of Attributes

      • Yi Yu Yao, Ning Zhong

      Pages 138-141

5. Clustering

   1. Prototype Generation Based on Instance Filtering and Averaging

      • Chi-Kin Keung, Wai Lam

      Pages 142-152

   2. A Visual Method of Cluster Validation with Fastmap

      • Zhexue Huang, Tao Lin

      Pages 153-164

   3. COE: Clustering with Obstacles Entities A Preliminary Study

      • Anthony K. H. Tung, Jean Hou, Jiawei Han

      Pages 165-168

The Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2000) was held at the Keihanna-Plaza, Kyoto, Japan, April 18 - 20, 2000. PAKDD 2000 provided an international forum for researchers and applica­ tion developers to share their original research results and practical development experiences. A wide range of current KDD topics were covered including ma­ chine learning, databases, statistics, knowledge acquisition, data visualization, knowledge-based systems, soft computing, and high performance computing. It followed the success of PAKDD 97 in Singapore, PAKDD 98 in Austraha, and PAKDD 99 in China by bringing together participants from universities, indus­ try, and government from all over the world to exchange problems and challenges and to disseminate the recently developed KDD techniques. This PAKDD 2000 proceedings volume addresses both current issues and novel approaches in regards to theory, methodology, and real world application. The technical sessions were organized according to subtopics such as Data Mining Theory, Feature Selection and Transformation, Clustering, Application of Data Mining, Association Rules, Induction, Text Mining, Web and Graph Mining. Of the 116 worldwide submissions, 33 regular papers and 16 short papers were accepted for presentation at the conference and included in this volume. Each submission was critically reviewed by two to four program committee members based on their relevance, originality, quality, and clarity.

• DOM
• Electronic Commerce
• Fuzzy
• calculus
• classification
• data mining
• databases
• knowledge discovery
• learning
• machine learning
• optimization
• programming
• proving
• visualization

• Graduate School of Systems Management, Universiy of Tsukuba, Tokyo, Japan

  Takao Terano

• Department of Computer Science and Engineering, Arizona State University, Tempe

  Huan Liu

• Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan ROC

  Arbee L. P. Chen