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Advances in Knowledge Discovery and Data Mining

7th Pacific-Asia Conference, PAKDD 2003. Seoul, Korea, April 30 - May 2, 2003, Proceedings

  • Conference proceedings
  • © 2003

Overview

Editors:
  1. Kyu-Young Whang

    1. Computer Science Department, Korea Advanced Institute of Science and Technology, Daejeon, Korea

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  2. Jongwoo Jeon

    1. Department of Statistics, Seoul National University, Sillimdong Kwanakgu, Seoul, Korea

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  3. Kyuseok Shim

    1. School of Electrical Engineering and Computer Science, Seoul National University, Seoul, Korea

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  4. Jaideep Srivastava

    1. Department of Computer Science and Engineering, University of Minnesota, Minneapolis, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 2637)

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

Included in the following conference series:

Conference proceedings info: PAKDD 2003.

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About this book

The 7th Paci?c Asia Conference on Knowledge Discovery and Data Mining (PAKDD) was held from April 30 to May 2, 2003 in the Convention and Ex- bition Center (COEX), Seoul, Korea. The PAKDD conference is a major forum for academic researchers and industry practitioners in the Paci?c Asia region to share original research results and development experiences from di?erent KDD-related areas such as data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition and discovery, data visualization, and knowledge-based systems. The conference was organized by the Advanced Information Technology Research Center (AITrc) at KAIST and the Statistical Research Center for Complex Systems (SRCCS) at Seoul National University. It was sponsored by the Korean Datamining Society (KDMS), the Korea Inf- mation Science Society (KISS), the United States Air Force O?ce of Scienti?c Research, the Asian O?ce of Aerospace Research & Development, and KAIST. It was held with cooperation from ACM’s Special Group on Knowledge Dis- very and Data Mining (SIGKDD).

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Table of contents (60 papers)

  1. Front Matter

    Pages I-XVIII
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  2. Industrial Papers (Invited)

    1. Data Mining as an Automated Service

      • P. S. Bradley
      Pages 1-13

    2. Trends and Challenges in the Industrial Applications of KDD

      • Ramasamy Uthurusamy
      Pages 14-14

  3. Stream Mining I

    1. Finding Event-Oriented Patterns in Long Temporal Sequences

      • Xingzhi Sun, Maria E. Orlowska, Xiaofang Zhou
      Pages 15-26

    2. Mining Frequent Episodes for Relating Financial Events and Stock Trends

      • Anny Ng, Ada Wai-chee Fu
      Pages 27-39

  4. Graph Mining

    1. An Efficient Algorithm of Frequent Connected Subgraph Extraction

      • Mingsheng Hong, Haofeng Zhou, Wei Wang, Baile Shi
      Pages 40-51

    2. Classifier Construction by Graph-Based Induction for Graph-Structured Data

      • Warodom Geamsakul, Takashi Matsuda, Tetsuya Yoshida, Hiroshi Motoda, Takashi Washio
      Pages 52-62

  5. Clustering I

    1. Comparison of the Performance of Center-Based Clustering Algorithms

      • Bin Zhang
      Pages 63-74

    2. Automatic Extraction of Clusters from Hierarchical Clustering Representations

      • Jörg Sander, Xuejie Qin, Zhiyong Lu, Nan Niu, Alex Kovarsky
      Pages 75-87

  7. Bio Mining

    1. Mining Patterns of Dyspepsia Symptoms Across Time Points Using Constraint Association Rules

      • Annie Lau, Siew Siew Ong, Ashesh Mahidadia, Achim Hoffmann, Johanna Westbrook, Tatjana Zrimec
      Pages 124-135

    2. Predicting Protein Structural Class from Closed Protein Sequences

      • N. Rattanakronkul, T. Wattarujeekrit, K. Waiyamai
      Pages 136-147

    3. Learning Rules to Extract Protein Interactions from Biomedical Text

      • Tu Minh Phuong, Doheon Lee, Kwang Hyung Lee
      Pages 148-158

    4. Predicting Protein Interactions in Human by Homologous Interactions in Yeast

      • Hyongguen Kim, Jong Park, Kyungsook Han
      Pages 159-165

  8. Web Mining

    1. Mining the Customer’s Up-To-Moment Preferences for E-commerce Recommendation

      • Yi-Dong Shen, Qiang Yang, Zhong Zhang, Hongjun Lu
      Pages 166-177

    2. A Graph-Based Optimization Algorithm for Website Topology Using Interesting Association Rules

      • Edmond H. Wu, Michael K. Ng
      Pages 178-190

    3. A Markovian Approach for Web User Profiling and Clustering

      • Younes Hafri, Chabane Djeraba, Peter Stanchev, Bruno Bachimont
      Pages 191-202
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Other volumes

  1. Advances in Knowledge Discovery and Data Mining

Editors and Affiliations

  • Computer Science Department, Korea Advanced Institute of Science and Technology, Daejeon, Korea

    Kyu-Young Whang

  • Department of Statistics, Seoul National University, Sillimdong Kwanakgu, Seoul, Korea

    Jongwoo Jeon

  • School of Electrical Engineering and Computer Science, Seoul National University, Seoul, Korea

    Kyuseok Shim

  • Department of Computer Science and Engineering, University of Minnesota, Minneapolis, USA

    Jaideep Srivastava

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