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Advances in Knowledge Discovery and Data Mining

10th Pacific-Asia Conference, PAKDD 2006, Singapore, April 9-12, 2006, Proceedings

  • Conference proceedings
  • © 2006

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Editors:
  1. Wee-Keong Ng

    1. Nanyang Technological University, Singapore

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  2. Masaru Kitsuregawa

    1. Institute of Industrial Science, The University of Tokyo, Tokyo, Japan

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  3. Jianzhong Li

    1. School of Computer Science and Technology, Heilongjiang University, China

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  4. Kuiyu Chang

    1. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 3918)

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

Included in the following conference series:

Conference proceedings info: PAKDD 2006.

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About this book

The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is a leading international conference in the area of data mining and knowledge discovery. This year marks the tenth anniversary of the successful annual series of PAKDD conferences held in the Asia Pacific region. It was with pleasure that we hosted PAKDD 2006 in Singapore again, since the inaugural PAKDD conference was held in Singapore in 1997. PAKDD 2006 continues its tradition of providing an international forum for researchers and industry practitioners to share their new ideas, original research results and practical development experiences from all aspects of KDD data mining, including data cleaning, data warehousing, data mining techniques, knowledge visualization, and data mining applications. This year, we received 501 paper submissions from 38 countries and regions in Asia, Australasia, North America and Europe, of which we accepted 67 (13.4%) papers as regular papers and 33 (6.6%) papers as short papers. The distribution of the accepted papers was as follows: USA (17%), China (16%), Taiwan (10%), Australia (10%), Japan (7%), Korea (7%), Germany (6%), Canada (5%), Hong Kong (3%), Singapore (3%), New Zealand (3%), France (3%), UK (2%), and the rest from various countries in the Asia Pacific region.

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Table of contents (101 papers)

  1. Front Matter

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  2. Keynote Speech

    1. Protection or Privacy? Data Mining and Personal Data

      • David J. Hand
      Pages 1-10

    2. The Changing Face of Web Search

      • Prabhakar Raghavan
      Pages 11-11

  3. Invited Speech

    1. Data Mining for Surveillance Applications

      • Bhavani M. Thuraisingham
      Pages 12-14

  4. Classification

    1. A Multiclass Classification Method Based on Output Design

      • Qi Qiang, Qinming He
      Pages 15-19

    2. Regularized Semi-supervised Classification on Manifold

      • Lianwei Zhao, Siwei Luo, Yanchang Zhao, Lingzhi Liao, Zhihai Wang
      Pages 20-29

    3. Similarity-Based Sparse Feature Extraction Using Local Manifold Learning

      • Cheong Hee Park
      Pages 30-34

    4. Generalized Conditional Entropy and a Metric Splitting Criterion for Decision Trees

      • Dan A. Simovici, Szymon Jaroszewicz
      Pages 35-44

    5. RNBL-MN: A Recursive Naive Bayes Learner for Sequence Classification

      • Dae-Ki Kang, Adrian Silvescu, Vasant Honavar
      Pages 45-54

    6. TRIPPER: Rule Learning Using Taxonomies

      • Flavian Vasile, Adrian Silvescu, Dae-Ki Kang, Vasant Honavar
      Pages 55-59

    7. Using Weighted Nearest Neighbor to Benefit from Unlabeled Data

      • Kurt Driessens, Peter Reutemann, Bernhard Pfahringer, Claire Leschi
      Pages 60-69

    8. Constructive Meta-level Feature Selection Method Based on Method Repositories

      • Hidenao Abe, Takahira Yamaguchi
      Pages 70-80

  5. Ensemble Learning

    1. Variable Randomness in Decision Tree Ensembles

      • Fei Tony Liu, Kai Ming Ting
      Pages 81-90

    2. Further Improving Emerging Pattern Based Classifiers Via Bagging

      • Hongjian Fan, Ming Fan, Kotagiri Ramamohanarao, Mengxu Liu
      Pages 91-96

    3. Improving on Bagging with Input Smearing

      • Eibe Frank, Bernhard Pfahringer
      Pages 97-106

    4. Boosting Prediction Accuracy on Imbalanced Datasets with SVM Ensembles

      • Yang Liu, Aijun An, Xiangji Huang
      Pages 107-118

  6. Ensemble Learning

    1. DeLi-Clu: Boosting Robustness, Completeness, Usability, and Efficiency of Hierarchical Clustering by a Closest Pair Ranking

      • Elke Achtert, Christian Böhm, Peer Kröger
      Pages 119-128

    2. Iterative Clustering Analysis for Grouping Missing Data in Gene Expression Profiles

      • Dae-Won Kim, Bo-Yeong Kang
      Pages 129-138

    3. An EM-Approach for Clustering Multi-Instance Objects

      • Hans-Peter Kriegel, Alexey Pryakhin, Matthias Schubert
      Pages 139-148

    4. Mining Maximal Correlated Member Clusters in High Dimensional Database

      • Lizheng Jiang, Dongqing Yang, Shiwei Tang, Xiuli Ma, Dehui Zhang
      Pages 149-159
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Other volumes

  1. Advances in Knowledge Discovery and Data Mining

Editors and Affiliations

  • Nanyang Technological University, Singapore

    Wee-Keong Ng

  • Institute of Industrial Science, The University of Tokyo, Tokyo, Japan

    Masaru Kitsuregawa

  • School of Computer Science and Technology, Heilongjiang University, China

    Jianzhong Li

  • School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Kuiyu Chang

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