Ebook: Data Mining and Knowledge Management: Chinese Academy of Sciences Symposium CASDMKM 2004, Beijing, China, July 12-14, 2004. Revised Papers

criteria linear and nonlinear programming has proven to be a very useful approach. • Knowledge management for enterprise: These papers address various issues related to the application of knowledge management in corporations using various techniques. A particular emphasis here is on coordination and cooperation. • Risk management: Better knowledge management also requires more advanced techniques for risk management, to identify, control, and minimize the impact of uncertain events, as shown in these papers, using fuzzy set theory and other approaches for better risk management. • Integration of data mining and knowledge management: As indicated earlier, the integration of these two research fields is still in the early stage. Nevertheless, as shown in the papers selected in this volume, researchers have endearored to integrate data mining methods such as neural networks with various aspects related to knowledge management, such as decision support systems and expert systems, for better knowledge management. September 2004 Yong Shi Weixuan Xu Zhengxin Chen CASDMKM 2004 Organization Hosted by Institute of Policy and Management at the Chinese Academy of Sciences Graduate School of the Chinese Academy of Sciences International Journal of Information Technology and Decision Making Sponsored by Chinese Academy of Sciences National Natural Science Foundation of China University of Nebraska at Omaha, USA Conference Chairs Weixuan Xu, Chinese Academy of Sciences, China Yong Shi, University of Nebraska at Omaha, USA Advisory Committee

This book constitutes the thoroughly refereed post-proceedings of the Chinese Academy of Sciences Symposium on Data Mining and Knowledge Discovery CASDMKD 2004, held in Beijing, China in July 2004.

The 25 revised full papers presented together with 3 invited keynote papers were carefully reviewed and selected from 60 submissions. The papers are organized in topical sections on data mining methodology, practical issues, data mining for bioinformatics, data mining applications, enterprise knowledge management, risk management, and integration of data mining and knowledge management.