Optimized clustering with statistical-based local model for replica management in DDM over grid

Abstract

With the increasing complexity of the forms of the data (unstructured, massive, real-time, and heterogeneous), Distributed Data mining (DDM) approaches encounters a significant problem over grid infrastructure. The paper has identified a challenging problem i.e. an effective replica management which cost maximum resources to process the data from the warehouse in distributed data mining. The proposed system introduces a technique which presents a unique clustering mechanism of the data extracted from the replica (warehouse) and applies a novel statistical-based local model in order to extract the non-repetitive and unique data for accomplishing faster response time during distributed data mining. Powered by optimization using genetic algorithm, the proposed system offers better response time with increasing traffic load as compared to the similar existing technique of distributed data mining.