The 2001 IEEE International Conference on Data Mining (ICDM '01) provides a forum for the sharing of original research results and practical development experiences among researchers and application developers from different data mining related areas such as machine learning, automated scientific discovery, statistics, pattern recognition, knowledge acquisition, soft computing, databases and data warehousing, data visualization, and knowledge-based systems. The conference seeks solutions to challenging problems facing the development of data mining systems, and shapes future directions of research by promoting high quality, novel and daring research findings. As an important part of the conference, the workshops program will focus on new research challenges and initiatives.
Topics related to the design, analysis and implementation of data mining theory, systems and applications are of interest. These include, but are not limited to the following areas:
Foundations and principles of data mining
Data mining algorithms and methods in traditional areas (such as classification, clustering, probabilistic modeling, and association analysis), and in new areas
Data and knowledge representation for data mining
Modeling of structured, textual, temporal, spatial, multimedia and Web data to support data mining
Complexity, efficiency, and scalability issues in data mining
Data pre-processing, data reduction, feature selection and feature transformation
Statistics and probability in large-scale data mining
Soft computing (including neural networks, fuzzy logic, evolutionary computation, and rough sets) and uncertainty management for data mining
Integration of data warehousing, OLAP and data mining
Man-machine interaction in data mining and visual data mining
Artificial intelligence contributions to data mining
High performance and distributed data mining
Machine learning, pattern recognition and automated scientific discovery
Quality assessment and interestingness metrics of data mining results
Process centric data mining and models of data mining process
Security and social impact of data mining
Emerging data mining applications, such as electronic commerce, Web mining and intelligent learning database systems
High quality papers in all data mining areas are solicited. Papers exploring new directions will receive a careful and supportive review. All submitted papers should be limited to a maximum of 6,000 words (approximately 20 A4 pages), and will be reviewed on the basis of technical quality, relevance to data mining, originality, significance, and clarity. Accepted papers will be published in the conference proceedings by the IEEE Computer Society Press. A selected number of ICDM '01 accepted papers will be expanded and revised for possible inclusion in the Knowledge and Information Systems journal by Springer-Verlag.
ICDM Best Paper Awards will be conferred on the authors of the best papers at the conference.
This page has been accessed times since June 30, 2000.
Last modified: July 01, 2000.
All paper submissions will be handled electronically. Detailed instructions are provided on the conference home page at http://kais.mines.edu/~xwu/icdm/icdm-01.html.
Conference Chair: ================= Xindong Wu, University of Vermont, USA Program Committee Chairs: ========================= Nick Cercone, University of Waterloo, Canada T.Y. Lin, San Jose State University, USA ICDM '01 Workshops Chair: ========================= Johannes Gehrke, Cornell University, USA ICDM '01 Tutorials Chair: ========================= Chris Clifton, MITRE, USA ICDM '01 Panels Chair: ====================== Ramamohanarao Kotagiri, University of Melbourne, Australia ICDM '01 Publicity Chair: ========================= Ning Zhong, Maebashi Institute of Technology, Japan ICDM '01 Local Arrangements Chair: ================================== Xiaohua (Tony) Hu, Vigilance Inc., USA
Dr. Xindong Wu Dept. of Mathematical and Computer Sciences, Colorado School of Mines, 1500 Illinois Street, Golden, Colorado 80401, USA. Telephone: +1-303-273-3874 Facsimile: +1-303-273-3875 E-mail: email@example.com