Knowledge Discovery and Data Mining. Current Issues and New Applications

1. Clustering

   1. Combining Sampling Technique with DBSCAN Algorithm for Clustering Large Spatial Databases

      • Shuigeng Zhou, Aoying Zhou, Jing Cao, Jin Wen, Ye Fan, Yunfa Hu

      Pages 169-172

   2. Predictive Adaptive Resonance Theory and Knowledge Discovery in Databases

      • Ah-Hwee Tan, Hui-Shin Vivien Soon

      Pages 173-176

   3. Improving Generalization Ability of Self-Generating Neural Networks Through Ensemble Averaging

      • Hirotaka Inoue, Hiroyuki Narihisa

      Pages 177-180

2. Application of Data Mining

   1. Attribute Transformations on Numerical Databases

      • Tsau Young Lin, Joseph Tremba

      Pages 181-192

   2. Efficient Detection of Local Interactions in the Cascade Model

      • Takashi Okada

      Pages 193-203

   3. Extracting Predictors of Corporate Bankruptcy: Empirical Study on Data Mining Methods

      • Cindy Yoshiko Shirata, Takao Terano

      Pages 204-207

   4. Evaluating Hypothesis-Driven Exception-Rule Discovery with Medical Data Sets

      • Einoshin Suzuki, Shusaku Tsumoto

      Pages 208-211

   5. Discovering Protein Functional Models Using Inductive Logic Programming

      • Takashi Ishikawa, Masayuki Numao, Takao Terano

      Pages 212-215

   6. Mining Web Transaction Patterns in an Electronic Commerce Environment

      • Ching-Huang Yun, Ming-Syan Chen

      Pages 216-219

3. Association Rules and Related Topics

   1. Making Use of the Most Expressive Jumping Emerging Patterns for Classification

      • Jinyan Li, Guozhu Dong, Kotagiri Ramamohanarao

      Pages 220-232

   2. Mining Structured Association Patterns from Databases

      • Hirofumi Matsuzawa, Takeshi Fukuda

      Pages 233-244

   3. Association Rules

      • Tao Zhang

      Pages 245-256

   4. Density-Based Mining of Quantitative Association Rules

      • David W. Cheung, Lian Wang, S. M. Yiu, Bo Zhou

      Pages 257-268

   5. AViz: A Visualization System for Discovering Numeric Association Rules

      • Jianchao Han, Nick Cercone

      Pages 269-280

   6. Discovering Unordered and Ordered Phrase Association Patterns for Text Mining

      • Ryoichi Fujino, Hiroki Arimura, Setsuo Arikawa

      Pages 281-293

   7. Using Random Walks for Mining Web Document Associations

      • K. Selçuk Candan, Wen-Syan Li

      Pages 294-305

4. Induction

   1. A Concurrent Approach to the Key-Preserving Attribute-Oriented Induction Method

      • Maybin K. Muyeba, John A. Keane

      Pages 306-316

   2. Scaling Up a Boosting-Based Learner via Adaptive Sampling

      • Carlos Domingo, Osamu Watanabe

      Pages 317-328

   3. Adaptive Boosting for Spatial Functions with Unstable Driving Attributes

      • Aleksandar Lazarevic, Tim Fiez, Zoran Obradovic

      Pages 329-340

   4. Robust Ensemble Learning for Data Mining

      • Gunnar Rätsch, Bernhard Schölkopf, Alexander Johannes Smola, Sebastian Mika, Takashi Onoda, Klaus-Robert Müller

      Pages 341-344

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

Policies and ethics