Analysis of Co-Occurrence Patterns in Data through Modular and Clan Decompositions of Gaifman Graphs

TL;DR

This paper introduces a novel approach using modular and clan decompositions of Gaifman graphs to analyze co-occurrence patterns in data, providing both theoretical insights and practical tools for enhanced data visualization and analysis.

Contribution

It presents a new method combining graph decompositions with data analysis, along with theoretical connections and an open-source implementation.

Findings

Theoretical link between graph decompositions and data mining.

Development of an open-source tool for co-occurrence pattern analysis.

Enhanced visualization of data co-occurrence patterns.

Abstract

We argue that the existing knowledge about modular decomposition of graphs and clan decomposition of 2-structures can be put to use advantageously in a context of data analysis. We show how to obtain visual descriptions of co-occurrence patterns by employing these decompositions on possibly generalized Gaifman graphs associated to datasets. We provide both theoretical advances that connect the proposed process to other data mining aspects (namely, closed set mining), as well as implemented algorithmics leading to an open-source tool that demonstrates our approach.