GBMST: An Efficient Minimum Spanning Tree Clustering Based on Granular-Ball Computing
This work addresses clustering challenges for data analysts by offering a more efficient and robust method, though it appears incremental as it builds on existing granular-ball and MST techniques.
The paper tackles the inefficiency and noise susceptibility of fine-grained clustering methods by proposing GBMST, an algorithm that combines multi-granularity granular-ball computing with minimum spanning trees, which accelerates MST construction and reduces outlier influence as shown in experiments on multiple datasets.
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.