A Fast Multiscale Clustering Approach Based on DBSCAN

• Published in: Wireless communications and mobile computing 2021, Vol.2021 (1)
• Main Authors: Chen, Runzi, Zhao, Shuliang, Liang, Meishe
• Format: Article
• Language: English
• Subjects: Algorithms; Clustering; Datasets; Methods
• ISSN: 1530-8669; 1530-8677
• DOI: 10.1155/2021/4071177

Multiscale brings great benefits for people to observe objects or problems from different perspectives. It has practical significance for clustering on multiscale data. At present, there is a lack of research on the clustering of large-scale data under the premise that clustering results of small-scale datasets have been obtained. If one does cluster on large-scale datasets by using traditional methods, two disadvantages are as follows: (1) Clustering results of small-scale datasets are not utilized. (2) Traditional method will cause more running overhead. Aims at these shortcomings, this paper proposes a multiscale clustering framework based on DBSCAN. This framework uses DBSCAN for clustering small-scale datasets, then introduces algorithm Scaling-Up Cluster Centers (SUCC) generating cluster centers of large-scale datasets by merging clustering results of small-scale datasets, not mining raw large-scale datasets. We show experimentally that, compared to traditional algorithm DBACAN and leading algorithms DBSCAN++ and HDBSCAN, SUCC can provide not only competitive performance but reduce computational cost. In addition, under the guidance of experts, the performance of SUCC is more competitive in accuracy.