A self-adaptive and robust fission clustering algorithm via heat diffusion and maximal turning angle

TL;DR

This paper introduces a self-adaptive, robust fission clustering algorithm that combines heat diffusion and maximal turning angle techniques, demonstrating improved performance on various datasets.

Contribution

It presents a novel self-adaptive robust fission clustering (SARFC) algorithm with an integrated noise identification method, enhancing clustering accuracy and robustness.

Findings

Outperforms existing clustering algorithms on multiple datasets.

Effectively identifies and handles noise in data.

Demonstrates robustness and adaptability in diverse scenarios.

Abstract

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.