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It has practical significance for clustering on multiscale data. At present, there is a lack of research on the clustering of large\u2010scale data under the premise that clustering results of small\u2010scale datasets have been obtained. If one does cluster on large\u2010scale datasets by using traditional methods, two disadvantages are as follows: (1) Clustering results of small\u2010scale 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\u2010scale datasets, then introduces algorithm Scaling\u2010Up Cluster Centers (SUCC) generating cluster centers of large\u2010scale datasets by merging clustering results of small\u2010scale datasets, not mining raw large\u2010scale 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. 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