GBMST: An Efficient Minimum Spanning Tree Clustering Based on Granular-Ball Computing

TL;DR

The paper introduces GBMST, a clustering algorithm that combines multi-granularity Granular-Ball and MST to improve efficiency and robustness against noise, demonstrated through experiments on various datasets.

Contribution

It presents a novel multi-granularity clustering method that enhances efficiency and noise resistance by integrating Granular-Ball computing with MST.

Findings

Effective in handling outliers

Accelerates MST construction

Performs well on multiple datasets

Abstract

Most of the existing clustering methods are based on a single granularity of information, such as the distance and density of each data. This most fine-grained based approach is usually inefficient and susceptible to noise. Therefore, we propose a clustering algorithm that combines multi-granularity Granular-Ball and minimum spanning tree (MST). We construct coarsegrained granular-balls, and then use granular-balls and MST to implement the clustering method based on "large-scale priority", which can greatly avoid the influence of outliers and accelerate the construction process of MST. Experimental results on several data sets demonstrate the power of the algorithm. All codes have been released at https://github.com/xjnine/GBMST.