  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.
