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

arXiv: https://arxiv.org/abs/2102.03794 · HTML: https://mlsanity.com/paper/2102.03794/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 48 references that are in the map.
- First posted on arXiv 7 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [Gaussian Parsimonious Clustering Models with Covariates and a Noise Component](https://mlsanity.com/paper/1711.05632/) (2017)
- [Investigation of Parameter Uncertainty in Clustering Using a Gaussian Mixture Model Via Jackknife, Bootstrap and Weighted Likelihood Bootstrap](https://mlsanity.com/paper/1510.00551/) (2015)
- [Clustering by the way of atomic fission](https://mlsanity.com/paper/1906.11416/) (2019)

## Abstract (arXiv)

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.