Integration of useful links in distributed databases using decision tree classification

Nowadays, distributed relational databases constitute a large part of information storage handled by a variety of users. The knowledge extraction from these databases has been studied massively during this last decade. However, the problem still present in the distributed data mining process is the communication cost between the different parts of the database located naturally in remote sites. We present in this paper a decision tree classification approach with a low cost communication strategy using a set of the most useful inter-base links for the classification task. Different experiments conducted on real datasets showed a significant reduction in communication costs and an accuracy almost identical to some traditional approaches.