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.
Highlights
Clustering is one of the vital data mining and machine learning techniques and that aims to group similar objects into the same cluster and separate dissimilar objects into different clusters [1]
We provide a mathematical model, design a novel algorithm named Scaling-Up Cluster Centers (SUCC) from small scale to large scale, which avoids repetitive clustering on raw datasets
Experimental results show that the SUCC is efficient and reduces runtime consumption with competitive accuracy compared to traditional methods and the leading algorithms, which need to deal with raw data that is much more than cluster centers belonged small-scale data in most instances
Summary
Clustering is one of the vital data mining and machine learning techniques and that aims to group similar objects into the same cluster and separate dissimilar objects into different clusters [1]. We concentrate on Scaling-Up Cluster Centers (SUCC) from small scale to large scale and avoid repetitive clustering on raw datasets, with competitive efficiency. It is inefficient that clustering at each scale data by using the traditional method, i.e., reclustering, while SUCC can improve efficiency by computing cluster centers belonged small-scale data and obtaining large scale’s clusters. We provide a mathematical model (multiscale clustering framework), design a novel algorithm named Scaling-Up Cluster Centers (SUCC) from small scale to large scale, which avoids repetitive clustering on raw datasets. Experimental results show that the SUCC is efficient and reduces runtime consumption with competitive accuracy compared to traditional methods and the leading algorithms, which need to deal with raw data that is much more than cluster centers belonged small-scale data in most instances.
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