On an Introduction of Numerical Patterns into Continuous Data and Its Application to Rough Sets Based Rule Generation

Abstract

Rough set theory has mainly been applied to data with categorical values. In order to handle data with numerical values, we define numerical patterns with two symbols # and @, and propose more flexible rough sets based rule generation. The concepts of coarse and fine for rules are explicitly defined according to numerical patterns. This paper focuses on the rough sets based method for rule generation, which is enhanced by numerical patterns, and refers to the tool programs. Tool programs are applied to data in UCI Machine Learning Repository, and some useful rules are obtained.