Rough Set Model for Discovering Hybrid Association Rules

TL;DR

This paper introduces the RSHAR algorithm that uses rough set theory to discover hybrid association rules across multiple database tables, enhancing rule generation and integration.

Contribution

The paper presents a novel rough set-based algorithm for mining hybrid association rules, combining table joining, mapping codes, and an Apriori-like search for candidate itemsets.

Findings

Effective rule discovery across multiple domains

Improved performance over traditional methods

Integration of rules into information systems

Abstract

In this paper, the mining of hybrid association rules with rough set approach is investigated as the algorithm RSHAR.The RSHAR algorithm is constituted of two steps mainly. At first, to join the participant tables into a general table to generate the rules which is expressing the relationship between two or more domains that belong to several different tables in a database. Then we apply the mapping code on selected dimension, which can be added directly into the information system as one certain attribute. To find the association rules, frequent itemsets are generated in second step where candidate itemsets are generated through equivalence classes and also transforming the mapping code in to real dimensions. The searching method for candidate itemset is similar to apriori algorithm. The analysis of the performance of algorithm has been carried out.