AN ALTERNATIVE PARAMETER FREE CLUSTERING ALGORITHM USING DATA POINT POSITIONING ANALYSIS (DPPA) - COMPARISON WITH DBSCAN

DBSCAN is one of the most popular clustering algorithms that could handle clusters which have characteristics of arbitrary shape, multiple densities and noises. How-ever, its accuracy depends on the right selection of the two parameters, MinPts and Eps. There have been numerous research works to overcome this issue by developing parameter free clustering algorithm. We propose a clustering algorithm which uses Data Point Po-sitioning Analysis (DPPA) to analyze the relationship of each point to all points based on two nearest neighbor concepts, namely 1-NN and Max-NN. The algorithm is applied on 13 benchmark datasets that have been applied in many clustering algorithms with three-dimensional data and subsequently on higher dimensional data with sixteen attributes. The performance of the algorithm is visually compared with the three-dimensional graph plotting at various angles to determine the actual number of clusters. For the higher dimensional data, Silhouette coefficient is used to measure the performance. For both ex-perimental results, the DPPA algorithm is compared against DBSCAN. The results show that the DPPA algorithm is comparable to the performance of DBSCAN algorithm such that it manages to detect arbitrary cluster shapes, identify the number of clusters and manage the data sets with noises.