Granular association rules for multi-valued data

TL;DR

This paper introduces techniques for mining positive granular association rules from multi-valued relational data, effectively filtering out less useful negative rules, demonstrated on the movielens dataset.

Contribution

It develops a method to filter negative rules in granular association rule mining, focusing on extracting more meaningful positive rules from multi-valued data.

Findings

Most rules in movielens data are negative.

The proposed filtering technique effectively reduces negative rules.

Positive rules mined are more relevant and useful.

Abstract

Granular association rule is a new approach to reveal patterns hide in many-to-many relationships of relational databases. Different types of data such as nominal, numeric and multi-valued ones should be dealt with in the process of rule mining. In this paper, we study multi-valued data and develop techniques to filter out strong however uninteresting rules. An example of such rule might be "male students rate movies released in 1990s that are NOT thriller." This kind of rules, called negative granular association rules, often overwhelms positive ones which are more useful. To address this issue, we filter out negative granules such as "NOT thriller" in the process of granule generation. In this way, only positive granular association rules are generated and strong ones are mined. Experimental results on the movielens data set indicate that most rules are negative, and our technique is effective to filter them out.