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arXiv 2303.01082cs.LGcs.AIcs.DB
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GBMST:一种基于粒球计算的高效最小生成树聚类算法

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

Chongqing University of Telecommunications and Posts(重庆邮电大学)

Jiang Xie, Shuyin Xia, Guoyin Wang, Xinbo Gao

摘要:

现有的大多数聚类方法都是基于单一粒度的信息,例如每个数据的距离和密度。这种基于最细粒度的方法通常效率低下,并且容易受到噪声的影响。因此,我们提出了一种结合多粒度粒度球和最小生成树(MST)的聚类算法。通过构造粗粒度的粒度球,利用粒度球和MST实现基于“大规模优先级”的聚类方法,大大避免了离群点的影响,加快了MST的构造过程。几个数据集上的实验结果证明了该算法的能力。所有代码已在https://github.com/xjnine/GBMST上发布。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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