Combining Clustering techniques and Formal Concept Analysis to characterize Interestingness Measures

TL;DR

This paper leverages Formal Concept Analysis combined with clustering techniques to categorize interestingness measures for association rules, aiding users in selecting relevant measures based on their behavior.

Contribution

It introduces a method to cluster interestingness measures using FCA and clustering algorithms, based on their semantic properties, to facilitate better measure selection.

Findings

FCA effectively groups measures with similar behavior.

Hierarchical and partitioning clustering methods validate measure clusters.

The study classifies 61 measures into meaningful groups.

Abstract

Formal Concept Analysis "FCA" is a data analysis method which enables to discover hidden knowledge existing in data. A kind of hidden knowledge extracted from data is association rules. Different quality measures were reported in the literature to extract only relevant association rules. Given a dataset, the choice of a good quality measure remains a challenging task for a user. Given a quality measures evaluation matrix according to semantic properties, this paper describes how FCA can highlight quality measures with similar behavior in order to help the user during his choice. The aim of this article is the discovery of Interestingness Measures "IM" clusters, able to validate those found due to the hierarchical and partitioning clustering methods "AHC" and "k-means". Then, based on the theoretical study of sixty one interestingness measures according to nineteen properties, proposed in a recent study, "FCA" describes several groups of measures.