Variance-based Clustering Technique for Distributed Data Mining Applications

Nowadays, huge amounts of data are naturally collected in distributed sites due to different facts and moving these data through the network for extracting useful knowledge is almost unfeasible for either technical reasons or policies. Furthermore, classical parallel algorithms cannot be applied, specially in loosely coupled environments. This requires to develop scalable distributed algorithms able to return the global knowledge by aggregating local results in an effective way. In this paper we propose a distributed algorithm based on independent local clustering processes and a global merging based on minimum variance increases and requires a limited communication overhead. We also introduce the notion of distributed sub-clusters perturbation to improve the global generated distribution. We show that this algorithm improves the quality of clustering compared to classical local centralized ones and is able to find real global data nature or distribution.