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Advances in Knowledge Discovery and Data Mining

6th Pacific-Asia Conference, PAKDD 2002, Taipei, Taiwan, May 6-8, 2002. Proceedings

  • Conference proceedings
  • © 2002

Overview

Editors:
  1. Ming-Syan Chen

    1. EE Department, National Taiwan University, Taipei, Taiwan, ROC

  2. Philip S. Yu

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

  3. Bing Liu

    1. School of Computing, National University of Singapore, Singapore

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 2336)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: PAKDD 2002.

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About this book

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).

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Table of contents (56 papers)

  1. Front Matter

    Pages I-XIII
    Download chapter PDF

  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
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Other volumes

  1. Advances in Knowledge Discovery and Data Mining

  2. Mining Multimedia and Complex Data

Editors and Affiliations

  • 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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