Mining Positive and Negative Association Rules Using CoherentApproach

TL;DR

This paper introduces a novel association rule mining framework that eliminates the need for setting a support threshold, aiming to improve rule quality and decision-making reliability.

Contribution

It proposes a new coherent approach for mining association rules without requiring a minimum support threshold, addressing issues of rule quality and user dependency.

Findings

Reduces reliance on support threshold setting

Enhances the quality of discovered association rules

Potentially improves decision-making based on rules

Abstract

In the data mining field, association rules are discovered having domain knowledge specified as a minimum support threshold. The accuracy in setting up this threshold directly influences the number and the quality of association rules discovered. Typically, before association rules are mined, a user needs to determine a support threshold in order to obtain only the frequent item sets. Having users to determine a support threshold attracts a number of issues. We propose an association rule mining framework that does not require a per-set support threshold. Often, the number of association rules, even though large in number, misses some interesting rules and the rules quality necessitates further analysis. As a result, decision making using these rules could lead to risky actions.