Clustering by the way of atomic fission

Cluster analysis, which focuses on the grouping and categorization of similar elements, is widely used in various fields of research. Inspired by the phenomenon of atomic fission, this paper proposes a novel density-based clustering algorithm, called fission clustering (FC). It focuses on mining the dense families of clusters in dataset and utilizes the information of the distance matrix to fissure clustering dataset into subsets. A K-nearest neighbors (KNN) local density indicator is applied to identify and remove the points of sparse areas so as to obtain a dense subset that is constituted by the dense families of clusters. The achieved algorithm by merging FC and KNN local density indicators is denoted as FC-KNN. Several frequently-used datasets were used to test the performance of this clustering approach, and to compare the results with those of other algorithms. The comprehensive comparisons indicate that our method has advantages over other common methods.