Proposition d'une nouvelle approche d'extraction des motifs fermés fréquents
This work addresses data mining challenges for handling large datasets, but it appears incremental as it builds on existing frequent pattern extraction methods.
The authors tackled the problem of extracting frequent closed itemsets from transaction databases by proposing a new approach called UFCIGs-DAC, which partitions the search space and simultaneously updates frequent closed patterns with their minimal generators, though no concrete performance numbers are provided.
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.