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

arXiv: https://arxiv.org/abs/2303.01082 · HTML: https://mlsanity.com/paper/2303.01082/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 42 references that are in the map.
- First posted on arXiv 2 Mar 2023.

## Builds on (most cited first, 2 of 2)

- [GBSVM: Granular-ball Support Vector Machine](https://mlsanity.com/paper/2210.03120/) (2022)
- [A Unified Framework for Representation-based Subspace Clustering of Out-of-sample and Large-scale Data](https://mlsanity.com/paper/1309.6487/) (2013)

## Built on by (most cited first, 1 of 1)

- [Granular-ball computing: an efficient, robust, and interpretable adaptive multi-granularity representation and computation method](https://mlsanity.com/paper/2304.11171/) (2023)

## Abstract (arXiv)

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.