# Proposition d'une nouvelle approche d'extraction des motifs ferm\'es fr\'equents

## TL;DR

This paper introduces UFCIGs-DAC, a novel method for efficiently extracting frequent closed itemsets and their minimal generators by partitioning the search space, enabling simultaneous exploration and updating of patterns.

## Contribution

It proposes a new approach that allows simultaneous exploration and updating of frequent closed patterns and their minimal generators through data partitioning.

## Key findings

- Implemented UFCIGs-DAC on test bases demonstrating effectiveness.
- Allows adaptation to any frequent closed pattern extraction algorithm.
- Enhances data analysis efficiency in large transaction databases.

## Abstract

This work is done as part of a master's thesis project. The increase in the volume of data has given rise to various issues related to the collection, storage, analysis and exploitation of these data in order to create an added value. In this master, we are interested in the search of frequent closed patterns in the transaction bases. One way to process data is to partition the search space into subcontexts, and then explore the subcontexts simultaneously. In this context, we have proposed a new approach for extracting frequent closed itemsets. The main idea is to update frequent closed patterns with their minimal generators by applying a strategy of partitioning of the initial extraction context. Our new approach called UFCIGs-DAC was designed and implemented to perform a search in the test bases. The main originality of this approach is the simultaneous exploration of the research space by the update of the frequent closed patterns and the minimal generators. Moreover, our approach can be adapted to any algorithm of extraction of the frequent closed patterns with their minimal generators.