An Efficient Spectral Clustering Algorithm Based on Granular-Ball

In order to solve the problem that the traditional spectral clustering algorithm is time-consuming and resource consuming when applied to large-scale data, resulting in poor clustering effect or even unable to cluster, this paper proposes a spectral clustering algorithm based on granular-ball(GBSC). The algorithm changes the construction method of the similarity matrix. Based on granular-ball, the size of the similarity matrix is greatly reduced, and the construction of the similarity matrix is more reasonable. Experimental results show that the proposed algorithm achieves better speedup ratio, less memory consumption and stronger anti noise performance while achieving similar clustering results to the traditional spectral clustering algorithm. Suppose the number of granular-balls is $m$ , $n$ is the number of points in the dataset, and $m< < n$ , the time complexity of GBSC is $O(m^{3})$ . It is proved that GBSC has good adaptability to large-scale datasets. All codes have been released at https://github.com/xjnine/GBSC .