Rough Sets Based Rule Generation from Data with Categorical and Numerical Values

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 in...

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Published in:Journal of advanced computational intelligence and intelligent informatics 2008-09, Vol.12 (5), p.426-434
Main Authors: Sakai, Hiroshi, Koba, Kazuhiro, Nakata, Michinori
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Language:English
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description 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.
doi_str_mv 10.20965/jaciii.2008.p0426
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title Rough Sets Based Rule Generation from Data with Categorical and Numerical Values
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