A novel density based clustering algorithm by incorporating mahalanobis distance

Abstract

Data clustering is one of the active research areas, which aims to group related data together. The process of data clustering improves the data organization and enhances the user experience as well. For this sake, several clustering algorithms are proposed in the literature. However, a constant demand for a better clustering algorithm is still a basic requirement. Understanding the necessity, this paper proposes a density based clustering algorithm which is based on Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The main drawback of DBSCAN algorithm is it requires two important parameters as initial input. It is really difficult to fix the values for these parameters, as it requires some prior knowledge about the dataset. This requirement is overthrown by the proposed clustering algorithm by selecting the parameters automatically. The automated selection of parameters is achieved by analysing the dataset and it varies from dataset to dataset. This way of parameter selection improves the quality of service and produce effective clusters. The experimental results show that the proposed approach outperforms the DBSCAN algorithm in terms of purity, F-measure and entropy.