Rough Sets and Association Rule Generation

Abstract

ASSOCIATION RULE (see [1]) extraction methods have been developed as the main methods for mining of real life data, in particular in Basket Data Analysis. In this paper we present a novel approach to generation of association rules, based on Rough Set and Boolean reasoning methods. Some results presented in this paper has been mentioned in [13, 17]. We will explain them precisely (with full proofs of theorems) in this paper. We show the relationship between the problems of association rule extraction for transaction data and relative reducts (or α-reducts generation) for a decision table. Moreover, the present approach can be used to extract association rules in general form. The experimental results show that the presented methods are quite efficient. Large number of association rules with given support and confidence can be extracted in a short time.