Block-Diagonal Guided DBSCAN Clustering

arXiv:2404.01341v1 Announce Type: new
Abstract: Cluster analysis plays a crucial role in database mining, and one of the most widely used algorithms in this field is DBSCAN. However, DBSCAN has several limitations, such as difficulty in handling high-dimensional large-scale data, sensitivity to input parameters, and lack of robustness in producing clustering results. This paper introduces an improved version of DBSCAN that leverages the block-diagonal property of the similarity graph to guide the clustering procedure of DBSCAN. The key idea is …

abstract algorithms analysis arxiv block cluster clustering cs.ai cs.ds cs.lg data database dbscan however limitations mining paper parameters results robustness role scale sensitivity type