Hi\'{e}rarchisation des r\`{e}gles d'association en fouille de textes

TL;DR

This paper introduces a hierarchical structuring method for association rules in text mining, enabling easier exploration and analysis by organizing rules into global and local hierarchies.

Contribution

It proposes a novel two-level hierarchy for association rules, combining Galois lattices and inductive logic programming for better rule management.

Findings

Hierarchies improve rule interpretability

Galois lattices used for global rule organization

Inductive logic programming enhances local rule analysis

Abstract

Extraction of association rules is widely used as a data mining method. However, one of the limit of this approach comes from the large number of extracted rules and the difficulty for a human expert to deal with the totality of these rules. We propose to solve this problem by structuring the set of rules into hierarchy. The expert can then therefore explore the rules, access from one rule to another one more general when we raise up in the hierarchy, and in other hand, or a more specific rules. Rules are structured at two levels. The global level aims at building a hierarchy from the set of rules extracted. Thus we define a first type of rule-subsomption relying on Galois lattices. The second level consists in a local and more detailed analysis of each rule. It generate for a given rule a set of generalization rules structured into a local hierarchy. This leads to the definition of a second type of subsomption. This subsomption comes from inductive logic programming and integrates a terminological model.