Rough Sets Based Rule Generation from Data with Categorical and Numerical Values
Hiroshi Sakai*, Kazuhiro Koba*, and Michinori Nakata**
*Department of Mathematics and Computer Aided Science, Faculty of Engineering, Kyushu Institute of Technology
Tobata, Kitakyushu 804-8550, Japan
**Faculty of Management and Information Science, Josai International University
Gumyo, Togane, Chiba 283-8555, Japan
Rough set theory has been mainly applied to data with categorical values. In order to handle data with numerical values in this theory, a familiar concept of ‘wildcards’ was employed, and a new framework of rough sets based rule generation has been proposed. Two characters @ and # were introduced into this framework, and numerical patterns were also defined for numerical values. The concepts of ‘coarse’ and ‘fine’ for rules were explicitly defined according to numerical patterns. This paper enhances the previous framework, and describes the implementation of an utility program. This utility program is applied to the data in UCI Machine Learning Repository, and some useful rules are obtained.
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