  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, a novel density-based clustering algorithm is
proposed in this paper, called fission clustering (FC). It focuses on mining
the dense families of a dataset and utilizes the information of the distance
matrix to fissure clustering dataset into subsets. When we face the dataset
which has a few points surround the dense families of clusters, K-nearest
neighbors local density indicator is applied to distinguish and remove the
points of sparse areas so as to obtain a dense subset that is constituted by
the dense families of clusters. A number of frequently-used datasets were used
to test the performance of this clustering approach, and to compare the results
with those of algorithms. The proposed algorithm is found to outperform other
algorithms in speed and accuracy.
