An Enhanced Density Based Spatial Clustering of Applications with Noise

Abstract

DBSCAN is a pioneer density based clustering algorithm. It can find out the clusters of different shapes and sizes from the large amount of data which is containing noise and outliers. But the clusters detected by it contain large amount of density variation within them. It can not handle the local density variation that exists within the cluster. For good clustering a significant density variation may be allowed within the cluster because if we go for homogeneous clustering, a large number of smaller unimportant clusters may be generated. In this paper we propose an Enhanced DBSCAN algorithm which keeps track of local density variation within the cluster. It calculates the density variance for any core object with respect to its e -neighborhood. If density variance of a core object is less than or equal to a threshold value and also satisfying the homogeneity index with respect to its e -neighborhood then it will allow the core object for expansion. The experimental results show that the proposed clustering algorithm gives optimized results.