  Cluster analysis, which focuses on the grouping and categorization of similar
elements, is widely used in various fields of research. A novel and fast
clustering algorithm, fission clustering algorithm, is proposed in recent year.
In this article, we propose a robust fission clustering (RFC) algorithm and a
self-adaptive noise identification method. The RFC and the self-adaptive noise
identification method are combine to propose a self-adaptive robust fission
clustering (SARFC) algorithm. Several frequently-used datasets were applied to
test the performance of the proposed clustering approach and to compare the
results with those of other algorithms. The comprehensive comparisons indicate
that the proposed method has advantages over other common methods.
