A Fast Multiscale Clustering Approach Based on DBSCAN

Abstract

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.

Two-step clustering-based pipeline for big dynamic functional network connectivity data

“…Unlike the mini-batch kmeans approach, our approach reduces the entire clustering process time ( Figure 5 ) and increases the clustering quality compared with standard kmeans ( Figure 7 ). Additionally, we can adapt mini-batch kmeans or other fast clustering approaches to our proposed dFNC pipeline ( Viswanath and Suresh Babu, 2009 ; Choromanska et al, 2013 ; Pourkamali-Anaraki and Becker, 2017 ; Chen et al, 2021 ). In other words, our new approach is a clustering algorithm agnostic pipeline.…”

“…We can adapt other fast clustering approaches to this pipeline and further improve the computational speed. Second, we did not compare our method’s computational speed and clustering quality with different fast clustering approaches ( Viswanath and Suresh Babu, 2009 ; Choromanska et al, 2013 ; Pourkamali-Anaraki and Becker, 2017 ; Chen et al, 2021 ). However, unlike these fast methods, our approach generated a better quality cluster than the standard kmeans clustering method.…”