A Density based clustering with Artificial Immunity inspired preprocessing

In this paper we propose an algorithm which can identify varied shaped clusters from wide variety of input dataset with high degree of accuracy in presence of noise. The initial data processing module adopts a novel approach of Artificial Immune system to reduce data redundancy while preserving the original data patterns. The clustering module pursues a density based approach to identify clusters from the compressed dataset produced by the preprocessing module. We introduced several new concepts like selective Antigenic binding, Local Reachability Factor, Global Reachability Factor to effectively recognize clusters with varied shape, varied density and low intercluster separation with acceptable computational cost. We performed experimental evaluation of our algorithm with wide variety of real and synthetic dataset and obtained higher cluster success rate for all dataset when compared to DBSCAN.