Advances in Knowledge Discovery and Data Mining

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

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.

• Bayesian network
• algorithms
• classification
• data mining
• knowledge discovery
• multimedia
• service-oriented computing
• web mining

• 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

Policies and ethics