Efficient density-based clustering using automatic parameter detection

Abstract

Clustering governs huge data by organizing similar data objects into groups. Density-based clustering permits composition of data objects on basis of their density distribution. DBSCAN, an illustrious and prominent density-based clustering algorithm gives birth to arbitrary-framed clusters, without requiring preexisting acquaintances on the number of clusters to be produced. The inputs to DBSCAN principal are: dataset required to be mined, radius of neighborhood—Eps (ε), minimum number of points needed to build a cluster (MinPts). DBSCAN clustering desires these two parameters to be given as input manually and automatic detection of these parameters is a very tedious exercise and has a significant influence on clustering result. In this paper, we contemplated a new and efficient density-based clustering algorithm (E-DBSCAN). The consolidated notion of the proposed approach is that it avoids manual intervention of input values. Experimental results demonstrate effectiveness and efficiency of the proposed algorithm on varied domain of datasets.