Minimum Cluster Size Estimation and Cluster Refinement for the Randomized Gravitational Clustering Algorithm

Abstract

Although clustering is an unsupervised learning approach, most clustering algorithms require setting several parameters (such as the number of clusters, minimum density or distance threshold) in advance to work properly. In this paper, we eliminate the necessity of setting the minimum cluster size parameter of the Randomized Gravitational Clustering algorithm proposed by Gomez et al. Basically, the minimum cluster size is estimated using a heuristic that takes in consideration the functional relation between the number of clusters and the clusters with at least a given number of points. Then a data point’s region of action (region of the space assigned to a point) is defined and a cluster refinement process is proposed in order to merge overlapping clusters. Our experimental results show that the proposed algorithm is able to deal with noise, while finding an appropriate number of clusters without requiring a manual setting of the minimum cluster size.