A Boolean Modeling for Improving the Algorithm Apriori

Mining association rules is one of the most important data mining tasks. Its purpose is to generate intelligible relations between attributes in a database. However, its use in practice is difficult and still raises several challenges, in particular, the number of learned rules is often very large. Several techniques for reducing the number of rules have been proposed as measures of quality, syntactic filtering constraints, etc. However, these techniques do not limit the shortcomings of these methods. In this paper, we propose a new approach to mine association, assisted by a Boolean modeling of results in order to mitigate the shortcomings mentioned above and propose a cellular automaton based on a boolean process for mining, optimizing, managing and representing of the learned rules.