Data Mining: How Research Meets Practical Development?

Authors:
Xindong Wu;S. Yu;Gregory Piatetsky-Shapiro;Nick Cercone;Y. Lin;Ramamohanarao Kotagiri;W. Wah
Affiliations:
University of Vermont, Department of Computer Science, USA and Department of Computer Science, University of Vermont, Burlington, VT 05405, USA;University of Vermont, Department of Computer Science, USA and IBM, T. J. Watson Research Center, Hawthorne, New York, USA;KDnuggets, Department of Computer Science, USA;University of Waterloo, School of Computer Science, Canada;San Jose State University, Department of Mathematics and Computer Science, USA;University of Melbourne, Department of Computer Science and Software Engineering, Australia;University of Illinois, Urbana-Champaign, Computer Systems Research Laboratory, USA
Venue:
Knowledge and Information Systems
Year:
2003

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Abstract

At the 2001 IEEE International Conference on Data Mining in San Jose, California, on November 29 to December 2, 2001, there was a panel discussion on how data mining research meets practical development. One of the motivations for organizing the panel discussion was to provide useful advice for industrial people to explore their directions in data mining development. Based on the panel discussion, this paper presents the views and arguments from the panel members, the Conference Chair and the Program Committee Co-Chairs. These people as a group have both academic and industrial experiences in different data mining related areas such as databases, machine learning, and neural networks. We will answer questions such as (1) how far data mining is from practical development, (2) how data mining research differs from practical development, and (3) what are the most promising areas in data mining for practical development.