Selective association rule generation

TL;DR

This paper proposes a novel method for association rule mining that focuses on pre-selecting interesting itemsets to generate rules, reducing the number of rules without raising support or confidence thresholds.

Contribution

It introduces a selective rule generation approach based on predefined interesting itemsets, improving efficiency and potentially capturing more relevant rules.

Findings

Significantly reduces the number of generated rules.

Avoids increasing support and confidence thresholds, preventing loss of important information.

Enhances the relevance of discovered rules by focusing on interesting itemsets.

Abstract

Mining association rules is a popular and well researched method for discovering interesting relations between variables in large databases. A practical problem is that at medium to low support values often a large number of frequent itemsets and an even larger number of association rules are found in a database. A widely used approach is to gradually increase minimum support and minimum confidence or to filter the found rules using increasingly strict constraints on additional measures of interestingness until the set of rules found is reduced to a manageable size. In this paper we describe a different approach which is based on the idea to first define a set of ``interesting'' itemsets (e.g., by a mixture of mining and expert knowledge) and then, in a second step to selectively generate rules for only these itemsets. The main advantage of this approach over increasing thresholds or filtering rules is that the number of rules found is significantly reduced while at the same time it is not necessary to increase the support and confidence thresholds which might lead to missing important information in the database.