DKDD_C: A clustering-based approach for distributed knowledge discovery

Abstract

In this paper, we address the problem of knowledge discovery. Several approaches have been proposed in this field. However, existing approaches generate a huge number of association rules that are difficult to exploit and assimilate. Moreover, they have not been proven themselves in a distributed context. As contribution, we propose, in this paper, DKDD_C, a new Distributed Knowledge Discovery approach. Exploiting, KDD based on data classification, we propose to give the choice to the user, either to generate Meta-Rules (rules between classes arising of preliminary data classification), or to generate classical Rules between distributed data. DKDD_C took place in both local and global processes. We prove that our solution minimizes the number of distributed generated association rules and then, offer a better interpretation of the data and optimization of the execution time. This approach has been validated by the implementation of a user-friendly platform as an extension of the Weka platform for the support of Distributed KDD.