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Advances in Knowledge Discovery and Data Mining

8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004, Proceedings

  • Conference proceedings
  • © 2004

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Editors:
  1. Honghua Dai

    1. School of Engineering and Information Technology, Deakin University, Australia

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  2. Ramakrishnan Srikant

    1. University of Illinois at Urbana-Champaign, Urbana, USA

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  3. Chengqi Zhang

    1. Faculty of Engineering and Information Technology, Centre for Quantum Computation and Intelligent Systems, and Australian ACS National Committee for Artificial Intelligence, University of Technology, Sydney, Australia

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 3056)

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

Included in the following conference series:

Conference proceedings info: PAKDD 2004.

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About this book

ThePaci?c-AsiaConferenceonKnowledgeDiscoveryandDataMining(PAKDD) has been held every year since 1997. This year, the eighth in the series (PAKDD 2004) was held at Carlton Crest Hotel, Sydney, Australia, 26–28 May 2004. PAKDD is a leading international conference in the area of data mining. It p- vides an international forum for researchers and industry practitioners to share their new ideas, original research results and practical development experiences from all KDD-related areas including data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition and automatic scienti?c discovery, data visualization, causal induction, and knowledge-based systems. The selection process this year was extremely competitive. We received 238 researchpapersfrom23countries,whichisthehighestinthehistoryofPAKDD, and re?ects the recognition of and interest in this conference. Each submitted research paper was reviewed by three members of the program committee. F- lowing this independent review, there were discussions among the reviewers, and when necessary, additional reviews from other experts were requested. A total of 50 papers were selected as full papers (21%), and another 31 were selected as short papers (13%), yielding a combined acceptance rate of approximately 34%. The conference accommodated both research papers presenting original - vestigation results and industrial papers reporting real data mining applications andsystemdevelopmentexperience.Theconferencealsoincludedthreetutorials on key technologies of knowledge discovery and data mining, and one workshop focusing on speci?c new challenges and emerging issues of knowledge discovery anddatamining.ThePAKDD2004programwasfurtherenhancedwithkeynote speeches by two outstanding researchers inthe area of knowledge discovery and data mining: Philip Yu, Manager of Software Tools and Techniques, IBM T.J.

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Table of contents (83 papers)

  1. Front Matter

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  2. Invited Speeches

    1. Mining of Evolving Data Streams with Privacy Preservation

      • Philip S. Yu
      Pages 1-1

    2. Data Mining Grand Challenges

      • Usama Fayyad
      Pages 2-2

  3. Session 1A: Classification (I)

    1. Evaluating the Replicability of Significance Tests for Comparing Learning Algorithms

      • Remco R. Bouckaert, Eibe Frank
      Pages 3-12

    2. Spectral Energy Minimization for Semi-supervised Learning

      • Chun-hung Li, Zhi-li Wu
      Pages 13-21

    3. Discriminative Methods for Multi-labeled Classification

      • Shantanu Godbole, Sunita Sarawagi
      Pages 22-30

  4. Session 1B: Clustering (I)

    1. Subspace Clustering of High Dimensional Spatial Data with Noises

      • Chih-Ming Hsu, Ming-Syan Chen
      Pages 31-40

    2. Constraint-Based Graph Clustering through Node Sequencing and Partitioning

      • Yu Qian, Kang Zhang, Wei Lai
      Pages 41-51

    3. Mining Expressive Process Models by Clustering Workflow Traces

      • Gianluigi Greco, Antonella Guzzo, Luigi Pontieri, Domenico Saccà
      Pages 52-62

  5. Session 1C: Association Rules (I)

    1. CMTreeMiner: Mining Both Closed and Maximal Frequent Subtrees

      • Yun Chi, Yirong Yang, Yi Xia, Richard R. Muntz
      Pages 63-73

    2. Secure Association Rule Sharing

      • Stanley R. M. Oliveira, Osmar R. Zaïane, Yücel Saygin
      Pages 74-85

    3. Self-Similar Mining of Time Association Rules

      • Daniel Barbará, Ping Chen, Zohreh Nazeri
      Pages 86-95

  6. Session 2A: Novel Algorithms (I)

    1. ParaDualMiner: An Efficient Parallel Implementation of the DualMiner Algorithm

      • Roger M. H. Ting, James Bailey, Kotagiri Ramamohanarao
      Pages 96-105

    2. A Novel Distributed Collaborative Filtering Algorithm and Its Implementation on P2P Overlay Network

      • Peng Han, Bo Xie, Fan Yang, Jiajun Wang, Ruimin Shen
      Pages 106-115

    3. An Efficient Algorithm for Dense Regions Discovery from Large-Scale Data Streams

      • Andy M. Yip, Edmond H. Wu, Michael K. Ng, Tony F. Chan
      Pages 116-120

    4. Blind Data Linkage Using n-gram Similarity Comparisons

      • Tim Churches, Peter Christen
      Pages 121-126

    5. Condensed Representation of Emerging Patterns

      • Arnaud Soulet, Bruno Crémilleux, François Rioult
      Pages 127-132

  7. Session 2B: Association (II)

    1. Discovery of Maximally Frequent Tag Tree Patterns with Contractible Variables from Semistructured Documents

      • Tetsuhiro Miyahara, Yusuke Suzuki, Takayoshi Shoudai, Tomoyuki Uchida, Kenichi Takahashi, Hiroaki Ueda
      Pages 133-144

    2. Mining Term Association Rules for Heuristic Query Construction

      • Zhenxing Qin, Li Liu, Shichao Zhang
      Pages 145-154

    3. FP-Bonsai: The Art of Growing and Pruning Small FP-Trees

      • Francesco Bonchi, Bart Goethals
      Pages 155-160
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Other volumes

  1. Advances in Knowledge Discovery and Data Mining

Editors and Affiliations

  • School of Engineering and Information Technology, Deakin University, Australia

    Honghua Dai

  • University of Illinois at Urbana-Champaign, Urbana, USA

    Ramakrishnan Srikant

  • Faculty of Engineering and Information Technology, Centre for Quantum Computation and Intelligent Systems, and Australian ACS National Committee for Artificial Intelligence, University of Technology, Sydney, Australia

    Chengqi Zhang

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