Class Association Rules Mining based Rough Set Method

TL;DR

This paper introduces an efficient rough set-based algorithm for mining class association rules, simplifying the process compared to traditional methods and demonstrating high effectiveness in data analysis.

Contribution

The paper proposes a novel rough set-inspired algorithm for class association rule mining, improving simplicity and efficiency over classic association rule methods.

Findings

Effective in discovering class association rules

Simpler than traditional association rule mining methods

Demonstrates high efficiency in data analysis

Abstract

This paper investigates the mining of class association rules with rough set approach. In data mining, an association occurs between two set of elements when one element set happen together with another. A class association rule set (CARs) is a subset of association rules with classes specified as their consequences. We present an efficient algorithm for mining the finest class rule set inspired form Apriori algorithm, where the support and confidence are computed based on the elementary set of lower approximation included in the property of rough set theory. Our proposed approach has been shown very effective, where the rough set approach for class association discovery is much simpler than the classic association method.