# Clustering by the way of atomic fission

## TL;DR

This paper introduces a novel density-based clustering algorithm inspired by atomic fission, which effectively identifies dense clusters by utilizing distance matrices and local density indicators, outperforming existing methods in speed and accuracy.

## Contribution

The paper proposes a new clustering algorithm called fission clustering (FC) that leverages atomic fission principles and local density measures to improve clustering performance.

## Key findings

- Outperforms existing algorithms in speed and accuracy
- Effectively identifies dense cluster families in datasets
- Demonstrated on multiple datasets with superior results

## Abstract

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.