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

• URL: http://arxiv.org/abs/2303.01082v2
• Date: Wed, 29 Mar 2023 05:25:48 GMT
• Title: GBMST: An Efficient Minimum Spanning Tree Clustering Based on Granular-Ball Computing
• Authors: Jiang Xie, Shuyin Xia, Guoyin Wang and Xinbo Gao
• Abstract summary: 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" Experimental results on several data sets demonstrate the power of the algorithm.
• Score: 78.92205914422925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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