Advances in Knowledge Discovery and Data Mining

1. Front Matter

   Pages I-XIII

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2. Industrial Papers (Invited)

   1. Network Data Mining and Analysis: The \( \mathcal{N}\mathcal{E}\mathcal{M}\mathcal{E}\mathcal{S}\mathcal{I}\mathcal{S} \) Project

      • Minos Garofalakis, Rajeev Rastogi

      Pages 1-12

   2. Privacy Preserving Data Mining: Challenges and Opportunities

      • Ramakrishnan Srikant

      Pages 13-13

3. Survey Papers (Invited)

   1. A Case for Analytical Customer Relationship Management

      • Jaideep Srivastava, Jau-Hwang Wang, Ee-Peng Lim, San-Yih Hwang

      Pages 14-27

   2. On Data Clustering Analysis: Scalability, Constraints, and Validation

      • Osmar R. Zaïane, Andrew Foss, Chi-Hoon Lee, Weinan Wang

      Pages 28-39

4. Association Rules (I)

   1. Discovering Numeric Association Rules via Evolutionary Algorithm

      • Jacinto Mata, José-Luis Alvarez, José-Cristobal Riquelme

      Pages 40-51

   2. Efficient Rule Retrieval and Postponed Restrict Operations for Association Rule Mining

      • Jochen Hipp, Christoph Mangold, Ulrich Güntzer, Gholamreza Nakhaeizadeh

      Pages 52-65

   3. Association Rule Mining on Remotely Sensed Images Using P-trees

      • Qin Ding, Qiang Ding, William Perrizo

      Pages 66-79

   4. On the Efficiency of Association-Rule Mining Algorithms

      • Vikram Pudi, Jayant R. Haritsa

      Pages 80-91

5. Classification (I)

   1. A Function-Based Classifier Learning Scheme Using Genetic Programming

      • Jung-Yi Lin, Been-Chian Chien, Tzung-Pei Hong

      Pages 92-103

   2. SNNB: A Selective Neighborhood Based Naïve Bayes for Lazy Learning

      • Zhipeng Xie, Wynne Hsu, Zongtian Liu, Mong Li Lee

      Pages 104-114

   3. A Method to Boost Naïve Bayesian Classifiers

      • Lili Diao, Keyun Hu, Yuchang Lu, Chunyi Shi

      Pages 115-122

   4. Toward Bayesian Classifiers with Accurate Probabilities

      • Charles X. Ling, Huajie Zhang

      Pages 123-134

6. Interestingness

   1. Pruning Redundant Association Rules Using Maximum Entropy Principle

      • Szymon Jaroszewicz, Dan A. Simovici

      Pages 135-147

   2. A Confidence-Lift Support Specification for Interesting Associations Mining

      • Wen-Yang Lin, Ming-Cheng Tseng, Ja-Hwung Su

      Pages 148-158

   3. Concise Representation of Frequent Patterns Based on Generalized Disjunction-Free Generators

      • Marzena Kryszkiewicz, Marcin Gajek

      Pages 159-171

   4. Mining Interesting Association Rules: A Data Mining Language

      • Show-Jane Yen, Yue-Shi Lee

      Pages 172-176

   5. The Lorenz Dominance Order as a Measure of Interestingness in KDD

      • Robert J. Hilderman

      Pages 177-185

7. Sequence Mining

   1. Efficient Algorithms for Incremental Update of Frequent Sequences

      • Minghua Zhang, Ben Kao, David Cheung, Chi-Lap Yip

      Pages 186-197

   2. DELISP: Efficient Discovery of Generalized Sequential Patterns by Delimited Pattern-Growth Technology

      • Ming-Yen Lin, Suh-Yin Lee, Sheng-Shun Wang

      Pages 198-209

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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