Advances in Knowledge Discovery and Data Mining

1. Sequence Mining

   1. Self-Similarity for Data Mining and Predictive Modeling A Case Study for Network Data

      • Jafar Adibi, Wei-Min Shen, Eaman Noorbakhsh

      Pages 210-217

   2. A New Mechanism of Mining Network Behavior

      • Shun-Chieh Lin, Shian-Shyong Tseng, Yao-Tsung Lin

      Pages 218-223

2. Clustering

   1. M-FastMap: A Modified FastMap Algorithm for Visual Cluster Validation in Data Mining

      • Michael Ng, Joshua Huang

      Pages 224-236

   2. An Incremental Hierarchical Data Clustering Algorithm Based on Gravity Theory

      • Chien-Yu Chen, Shien-Ching Hwang, Yen-Jen Oyang

      Pages 237-250

   3. Adding Personality to Information Clustering

      • Ah-Hwee Tan, Hong Pan

      Pages 251-256

   4. Clustering Large Categorical Data

      • François-Xavier Jollois, Mohamed Nadif

      Pages 257-263

3. Web Mining

   1. WebFrame: In Pursuit of Computationally and Cognitively Efficient Web Mining

      • Tong Zheng, Yonghe Niu, Randy Goebel

      Pages 264-275

   2. Naviz:Website Navigational Behavior Visualizer

      • Bowo Prasetyo, Iko Pramudiono, Katsumi Takahashi, Masaru Kitsuregawa

      Pages 276-289

   3. Optimal Algorithms for Finding User Access Sessions from Very Large Web Logs

      • Zhixiang Chen, Ada Wai-Chee Fu, Frank Chi-Hung Tong

      Pages 290-296

   4. Automatic Information Extraction for Multiple Singular Web Pages

      • Chia-Hui Chang, Shih-Chien Kuo, Kuo-Yu Hwang, Tsung-Hsin Ho, Chih-Lung Lin

      Pages 297-303

4. Association Rules (II)

   1. An Improved Approach for the Discovery of Causal Models via MML

      • Honghua Dai, Gang Li

      Pages 304-315

   2. SETM*-MaxK: An Efficient SET-Based Approach to Find the Largest Itemset

      • Ye-In Chang, Yu-Ming Hsieh

      Pages 316-321

   3. Discovery of Ordinal Association Rules

      • Sylvie Guillaume

      Pages 322-327

   4. Value Added Association Rules

      • T. Y. Lin, Y. Y. Yao, E. Louie

      Pages 328-333

   5. Top Down FP-Growth for Association Rule Mining

      • Ke Wang, Liu Tang, Jiawei Han, Junqiang Liu

      Pages 334-340

5. Semi-structure & Concept Mining

   1. Discovery of Frequent Tag Tree Patterns in Semistructured Web Documents

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

      Pages 341-355

   2. Extracting Characteristic Structures among Words in Semistructured Documents

      • Kazuyoshi Furukawa, Tomoyuki Uchida, Kazuya Yamada, Tetsuhiro Miyahara, Takayoshi Shoudai, Yasuaki Nakamura

      Pages 356-367

   3. An Efficient Algorithm for Incremental Update of Concept Spaces

      • Felix Cheung, Ben Kao, David Cheung, Chi-Yuen Ng

      Pages 368-380

6. Data Warehouse and Data Cube

   1. Efficient Constraint-Based Exploratory Mining on Large Data Cubes

      • Cuiping Li, Shengen Li, Shan Wang, Xiaoyong Du

      Pages 381-392

   2. Efficient Utilization of Materialized Views in a Data Warehouse

      • Don-Lin Yang, Man-Lin Huang, Ming-Chuan Hung

      Pages 393-404

Knowledge discovery and data mining have become areas of growing significance because of the recent increasing demand for KDD techniques, including those used in machine learning, databases, statistics, knowledge acquisition, data visualization, and high performance computing. In view of this, and following the success of the five previous PAKDD conferences, the sixth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2002) aimed to provide a forum for the sharing of original research results, innovative ideas, state-of-the-art developments, and implementation experiences in knowledge discovery and data mining among researchers in academic and industrial organizations. Much work went into preparing a program of high quality. We received 128 submissions. Every paper was reviewed by 3 program committee members, and 32 were selected as regular papers and 20 were selected as short papers, representing a 25% acceptance rate for regular papers. The PAKDD 2002 program was further enhanced by two keynote speeches, delivered by Vipin Kumar from the Univ. of Minnesota and Rajeev Rastogi from AT&T. In addition, PAKDD 2002 was complemented by three tutorials, XML and data mining (by Kyuseok Shim and Surajit Chadhuri), mining customer data across various customer touchpoints at- commerce sites (by Jaideep Srivastava), and data clustering analysis, from simple groupings to scalable clustering with constraints (by Osmar Zaiane and Andrew Foss).

• classification
• clustering
• data mining
• data warehouse
• knowledge
• knowledge discovery
• web mining

• EE Department, National Taiwan University, Taipei, Taiwan, ROC

  Ming-Syan Chen

• IBM Thomas J. Watson Research Center, Hawthorne, USA

  Philip S. Yu

• School of Computing, National University of Singapore, Singapore

  Bing Liu

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