Geometrical Approach to a New Hybrid Grid-Based Gravitational Clustering Algorithm

In the past years, several clustering algorithms have been developed, for example, K-means, K-medoid. Most of these algorithms have the common problem of selecting the appropriate number of clusters and these algorithms are sensitive to noisy data and would cause less accurate clustering of the data set. Therefore, this paper introduces a new Hybrid Grid-based Gravitational Clustering Algorithm (HGGCA) geometrically, which can automatically detect the number of clusters of the targeted data set and find the clusters with any arbitrary forms and filter the noisy data. This proposed clustering algorithm is used to move the cluster centers to the areas where the data density is high based on Newton’s law of gravity and Newton’s laws of motion. Also, the proposed method has higher accuracy than the existing K-means and K-medoids methods which is shown in the experimental result. In this study, we used cluster-validity-indicators to verify the validity of the proposed and existing methods of clustering. Experimental results show that the proposed algorithm massively creates high-quality clusters.