Using Taxonomies to Facilitate the Analysis of the Association Rules

TL;DR

This paper introduces the GART algorithm and RulEE-GAR module, which use taxonomies to generalize and analyze association rules, simplifying pattern interpretation in large datasets.

Contribution

It presents a novel method employing taxonomies for post-processing and analyzing association rules, enhancing interpretability of large pattern sets.

Findings

GART algorithm effectively generalizes association rules.

RulEE-GAR module facilitates rule analysis.

Improves understanding of large pattern collections.

Abstract

The Data Mining process enables the end users to analyze, understand and use the extracted knowledge in an intelligent system or to support in the decision-making processes. However, many algorithms used in the process encounter large quantities of patterns, complicating the analysis of the patterns. This fact occurs with association rules, a Data Mining technique that tries to identify intrinsic patterns in large data sets. A method that can help the analysis of the association rules is the use of taxonomies in the step of post-processing knowledge. In this paper, the GART algorithm is proposed, which uses taxonomies to generalize association rules, and the RulEE-GAR computational module, that enables the analysis of the generalized rules.