Download PDF Version
Important Dates
The Sixth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-02) will provide an international forum for the sharing of original research results and practical development experiences among researchers and application developers from different KDD related areas such as data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition and discovery, data visualization, and knowledge-based systems. Areas of interest include, but are not limited to:

* Data and Knowledge Representation
* Expanding the Autonomy of Machine Discovers
* Scientific Discovery
* Machine Learning Methods
* Statistical Methods
* Inductive Logic Programming
* Deduction, Induction and Abduction
* Multi-criteria Evaluation and Data Mining Metrics
* Models and Framework of New Data Mining Capabilities
* Performance Evaluation of Mining Algorithms
* Data and Dimensionality Reduction
* Preprocessing and Postprocessing
* Interestingness Checking of Data and Rules
* Management and Refinement for the Discovered Knowledge
* Data and Knowledge Visualization
* On Line Analytical Processing
* KDD Process and Human Interaction
* Rough Sets in Data Mining
* Neural Networks, Probabilistic Reasoning
* Noise Handling and Uncertainty Management
* Hybrid Symbolic/Connectionist KDD Systems
* Multi-Database Mining
* Data Mining in Advanced Databases (OODB, Spatial DB, Multimedia DB)
* Integration of Data Warehousing, OLAP and Data Mining
* Combining Data Mining with Database Querying
* Parallel and Distributed Data Mining
* Data Mining in the WWW
* Multi-agent, Multi-task KDD Systems
* Data Mining from Unstructured and Multimedia Data
* Unification of Data Mining with Intelligent Information Retrieval
* Security and Privacy Issues
* Successful/Innovative KDD Applications in Science, Engineering, Medicine, Business, Education, Government, and Industry
Steering Committees:
Hongjun Lu, Hong Kong University of Science & Technology, Hong Kong (chair)
Hiroshi Motoda, Osaka University, Japan (co-chair)
David W. Cheung, The Univerity Hong Kong, Hong Kong
Masaru Kitsuregawa, The University of Tokyo, Japan
Rao Kotagiri, University of Melbourne, Australia
Huan Liu, Arizona State University, USA
Takao Terano, University of Tsukuba, Japan
Graham Williams, CSIRO, Australia
Ning Zhong, Maebashi Institute of Technology, Japan
Lizhu Zhou, Tsinghua University, China
 Honorary Chairs:
David C. L. Liu, National Tsing Hua University, Taiwan
Ben Wah, University of Illinois, Urbana-Champaign, USA
Conference Co-Chairs:
Arbee L. P. Chen, National Tsing Hua University, Taiwan
Jiawei Han, Simon Fraser University, Canada
Program Chair/Co-Chair:
Ming-Syan Chen (Chair), National Taiwan University, Taiwan
Philip S. Yu, IBM Thomas J. Watson Research Center, U.S.A.
Bing Liu, National University of Singapore, Singapore
Tutorial Chairs:
Yao-Nan Lien, National Chengchi University, Taiwan
Industrial Chairs:
Chia-Hui Chang, National Central University, Taiwan
Vincent S. M. Tseng, National Cheng Kung University, Taiwan
Paper Submission:
The paper should consist of a cover page with title, authors' names, postal and e-mail addresses, an approximately 200 word summary, up to 5 keywords and a body not longer than 12 pages with a single space. (Formatting information for author is available from the Springer-Verlag web site at http://www.springer.de/comp/lncs/authors.html.) Please upload a softcopy of the paper to http://arbor.ee.ntu.edu.tw/pakdd02/ (in PostScript or PDF). Only when on-line submissions are not possible, should five copies of the paper be sent to the program chair at the following address:

Professor Ming-Syan Chen
Program Chair, PAKDD 2002
Department of Electrical Engineering
National Taiwan University, Taipei, Taiwan, ROC
Email: mschen@cc.ee.ntu.edu.tw
Tel: +886-2-23635251 ext 523
Fax: +886-2-23671597

Important Dates:
Paper submission due: Extended to November 15, 2001
Author notification: January 15, 2002
Camera ready copy due: February 5, 2002

All submitted papers will be reviewed on the basis of technical quality, relevance to KDD, originality, significance, and clarity. Accepted papers are expected to be published in the conference proceedings by Springer-Verlag in the Lecture Notes in Artificial Intelligence series.