Research and Development in Knowledge Discovery and Data Mining

1. Front Matter

   PDF

2. Knowledge acquisition for goal prediction in a multi-user adventure game

   • D. W. Albrecht, A. E. Nicholson, I. Zukerman

   Pages 1-12

3. Hybrid data mining systems: The next generation

   • Sarabjot S. Anand, John G. Hughes

   Pages 13-24

4. Discovering case knowledge using data mining

   • S. S. Anand, D. Patterson, J. G. Hughes, D. A. Bell

   Pages 25-35

5. Discovery of association rules over ordinal data: A new and faster algorithm and its application to basket analysis

   • Oliver Büchter, Rüdiger Wirth

   Pages 36-47

6. Effect of data skewness in parallel mining of association rules

   • David W. Cheung, Yongqiao Xiao

   Pages 48-60

7. Trend directed learning: A case study

   • Honghua Dai

   Pages 61-71

8. Interestingness of discovered association rules in terms of neighborhood-based unexpectedness

   • Guozhu Dong, Jinyan Li

   Pages 72-86

9. Point estimation using the Kullback-Leibler loss function and MML

   • David L. Dowe, Rohan A. Baxter, Jonathan J. Oliver, Chris S. Wallace

   Pages 87-95

10. Single factor analysis in MML mixture modelling

    • Russell T. Edwards, David L. Dowe

    Pages 96-109

11. Discovering associations in spatial data — An efficient medoid based approach

    • Vladimir Estivill-Castrol, Alan T. Murray

    Pages 110-121

12. Data mining using dynamically constructed recurrent fuzzy neural networks

    • Yakov Frayman, Lipo Wang

    Pages 122-131

13. CCAIIA: Clustering categorical attributes into interesting association rules

    • Brett Gray, M. E. Orlowska

    Pages 132-143

14. Selective materialization: An efficient method for spatial data cube construction

    • Jiawei Han, Nebojsa Stefanovic, Krzysztof Koperski

    Pages 144-158

15. Mining market basket data using share measures and characterized itemsets

    • Robert J. Hilderman, Colin L. Carter, Howard J. Hamilton, Nick Cercone

    Pages 159-173

16. Automatic visualization method for visual data mining

    • Yuichi Iizuka, Hisako Shiohara, Tetsuya Iizuka, Seiji Isobe

    Pages 174-185

17. Rough-set inspired approach to knowledge discovery in business databases

    • W. Kowalczyk, Z. Piasta

    Pages 186-197

18. Representative association rules

    • Marzena Kryszkiewicz

    Pages 198-209

19. Identifying relevant databases for multidatabase mining

    • Huan Liu, Hongjun Lu, Jun Yao

    Pages 210-221

20. Minimum message length segmentation

    • Jonathan J. Oliver, Rohan A. Baxter, Chris S. Wallace

    Pages 222-233

This book constitutes the refereed proceedings of the Second Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD-98, held in Melbourne, Australia, in April 1998. The book presents 30 revised full papers selected from a total of 110 submissions; also included are 20 poster presentations. The papers contribute new results to all current aspects in knowledge discovery and data mining on the research level as well as on the level of systems development. Among the areas covered are machine learning, information systems, the Internet, statistics, knowledge acquisition, data visualization, software reengineering, and knowledge based systems.

• Racter
• artificial intelligence
• classification
• data mining
• databases
• decision support system
• information system
• knowledge
• knowledge base
• knowledge discovery
• machine learning
• modeling
• neural network
• uncertainty
• visualization

• School of Computer Science and Software Engineering, Monash university, Caulfield East, Australia

  Xindong Wu

• Department of Computer Science, The University of Melbourne, Parkville, Australia

  Ramamohanarao Kotagiri

• School of Computer Science and Engineering, Monash university, Clayton, Australia

  Kevin B. Korb

Policies and ethics