Meningitis data mining by cooperatively using GDT-RS and RSBR

Authors:
Ning Zhong;Ju-Zhen Dong;Setsuo Ohsuga
Affiliations:
Knowledge Information Systems Laboratory, Department of Information Engineering, Maebashi Institute of Technology, 460-1, Kamisadori-Cho, Maebashi-City 371-0816, Japan;Knowledge Information Systems Laboratory, Department of Information Engineering, Maebashi Institute of Technology, 460-1, Kamisadori-Cho, Maebashi-City 371-0816, Japan;Department of Information and Computer Science, Waseda University, 3-4-1 Okubo Shinjuku-Ku, Tokyo 169, Japan
Venue:
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Year:
2003

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Abstract

This paper describes an application of two rough sets based systems, namely generalized distribution table and rough set (GDT-RS) and rough sets with Boolean reasoning (RSBR) respectively, for mining if-then rules in a meningitis dataset. GDT-RS is a soft hybrid induction system, and RSBR is used for discretization of real valued attributes as a pre-processing step realized before the GDT-RS starts. We argue that discretization of continuous valued attributes is an important pre-processing step in the rule discovery process. We illustrate the quality of rules discovered by GDT-RS is strongly affected by the result of discretization.