Neighborhood Based Clustering Method for Arbitrary Shaped Clusters

Abstract

Discovering clusters of arbitrary shape with variable densities is an interesting challenge in many fields of science and technology. There are few clustering methods, which can detect clusters of arbitrary shape and different densities. However, these methods are very sensitive with parameter settings and are not scalable with large datasets. In this paper, we propose a clustering method, which detects clusters of arbitrary shapes, sizes and different densities. We introduce a parameter termed $Nearest \mbox{ }Neighbor \mbox{ }Factor\mbox{ }(NNF)$ to determine relative position of an object in its neighborhood region. Based on relative position of a point, proposed method expands a cluster recursively or declares the point as outlier. Proposed method outperforms a classical method DBSCAN and recently proposed TI-k-Neighborhood-Index supported NBC method.