Advances in Knowledge Discovery and Data Mining

1. Front Matter

   PDF

2. Keynote Speech and Invited Talks

   1. Machine Learning for Analyzing Human Brain Function

      • Tom Mitchell

      Pages 1-1

   2. Subgroup Discovery Techniques and Applications

      • Nada Lavrač

      Pages 2-14

   3. IT Development in the 21st Century and Its Implications

      • Unna Huh

      Pages 15-20

3. Theoretic Foundations

   1. Data Mining of Gene Expression Microarray via Weighted Prefix Trees

      • Tran Trang, Nguyen Cam Chi, Hoang Ngoc Minh

      Pages 21-31

   2. Automatic Extraction of Low Frequency Bilingual Word Pairs from Parallel Corpora with Various Languages

      • Hiroshi Echizen-ya, Kenji Araki, Yoshio Momouchi

      Pages 32-37

   3. A Kernel Function Method in Clustering

      • Ling Zhang, Tao Wu, Yanping Zhang

      Pages 38-42

   4. Performance Measurements for Privacy Preserving Data Mining

      • Nan Zhang, Wei Zhao, Jianer Chen

      Pages 43-49

   5. Extraction of Frequent Few-Overlapped Monotone DNF Formulas with Depth-First Pruning

      • Yoshikazu Shima, Kouichi Hirata, Masateru Harao

      Pages 50-60

4. Association Rules

   1. Rule Extraction from Trained Support Vector Machines

      • Ying Zhang, HongYe Su, Tao Jia, Jian Chu

      Pages 61-70

   2. Pruning Derivative Partial Rules During Impact Rule Discovery

      • Shiying Huang, Geoffrey I. Webb

      Pages 71-80

   3. \(\mathcal{IGB}\): A New Informative Generic Base of Association Rules

      • Gh. Gasmi, S. Ben Yahia, E. Mephu Nguifo, Y. Slimani

      Pages 81-90

   4. A Divide and Conquer Approach for Deriving Partially Ordered Sub-structures

      • S. Ben Yahia, Y. Slimani, J. Rezgui

      Pages 91-96

   5. Finding Sporadic Rules Using Apriori-Inverse

      • Yun Sing Koh, Nathan Rountree

      Pages 97-106

   6. Automatic View Selection: An Application to Image Mining

      • Manoranjan Dash, Deepak Kolippakkam

      Pages 107-113

   7. Pushing Tougher Constraints in Frequent Pattern Mining

      • Francesco Bonchi, Claudio Lucchese

      Pages 114-124

   8. An Efficient Compression Technique for Frequent Itemset Generation in Association Rule Mining

      • Mafruz Zaman Ashrafi, David Taniar, Kate Smith

      Pages 125-135

   9. Mining Time-Profiled Associations: An Extended Abstract

      • Jin Soung Yoo, Pusheng Zhang, Shashi Shekhar

      Pages 136-142

   10. Online Algorithms for Mining Inter-stream Associations from Large Sensor Networks

       • K. K. Loo, Ivy Tong, Ben Kao

       Pages 143-149

   11. Mining Frequent Ordered Patterns

       • Zhi-Hong Deng, Cong-Rui Ji, Ming Zhang, Shi-Wei Tang

       Pages 150-154

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. It provides 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 scientific discovery, data visualization, causality induction, and knowledge-based systems. This year’s conference (PAKDD 2005) was the ninth of the PAKDD series, and carried the tradition in providing high-quality technical programs to facilitate research in knowledge discovery and data mining. It was held in Hanoi, Vietnam at the Melia Hotel, 18–20 May 2005. We are pleased to provide some statistics about PAKDD 2005. This year we received 327 submissions (a 37% increase over PAKDD 2004), which is the highest number of submissions since the first PAKDD in 1997) from 28 countries/regions: Australia (33), Austria (1), Belgium (2), Canada (11), China (91), Switzerland (2), France (9), Finland (1), Germany (5), Hong Kong (11), Indonesia (1), India (2), Italy (2), Japan (21), Korea (51), Malaysia (1), Macau (1), New Zealand (3), Poland (4), Pakistan (1), Portugal (3), Singapore (12), Taiwan (19), Thailand (7), Tunisia (2), UK (5), USA (31), and Vietnam (9). The submitted papers went through a rigorous reviewing process. Each submission was reviewed by at least two reviewers, and most of them by three or four reviewers.

• Clustering
• algorithmic learning
• association rule mining
• biomedical data analysis
• classification
• data mining
• information extraction
• knowledge discovery
• learning classifier systems
• machine learning
• rough sets
• self-organizing map
• text mining
• web data mining

• Japan Advanced Institute of Science and Technology, Nomi, Japan

  Tu Bao Ho

• University of Hong Kong, Hong Kong, China

  David Cheung

• Department of Computer Science and Engineering, Arizona State University, Tempe, USA

  Huan Liu

Policies and ethics