Characterization and extraction of condensed representation of correlated patterns based on formal concept analysis

TL;DR

This paper introduces a novel method for extracting condensed representations of both frequent and rare correlated patterns in data mining, utilizing Formal Concept Analysis to improve pattern summarization.

Contribution

It proposes a new approach combining formal concept analysis with the bond measure to efficiently represent correlated patterns without information loss.

Findings

Effective extraction of condensed pattern representations

Inclusion of both frequent and rare correlated patterns

Utilization of FCA for pattern analysis

Abstract

Correlated pattern mining has increasingly become an important task in data mining since these patterns allow conveying knowledge about meaningful and surprising relations among data. Frequent correlated patterns were thoroughly studied in the literature. In this thesis, we propose to benefit from both frequent correlated as well as rare correlated patterns according to the bond correlation measure. We propose to extract a subset without information loss of the sets of frequent correlated and of rare correlated patterns, this subset is called ``Condensed Representation``. In this regard, we are based on the notions derived from the Formal Concept Analysis FCA, specifically the equivalence classes associated to a closure operator fbond dedicated to the bond measure, to introduce new concise representations of both frequent correlated and rare correlated patterns.