A self-adaptive and robust fission clustering algorithm via heat diffusion and maximal turning angle
This paper addresses the problem of robust and self-adaptive clustering for researchers and practitioners in various fields who need to group similar elements from data, offering an incremental improvement to existing fission clustering methods.
This paper proposes a self-adaptive robust fission clustering (SARFC) algorithm that combines a robust fission clustering (RFC) algorithm with a self-adaptive noise identification method. The authors claim that their method outperforms other common clustering algorithms on several frequently-used datasets.
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.