A Fast Multiscale Clustering Approach Based on DBSCAN
| Autor: | Runzi Chen, Shuliang Zhao, Meishe Liang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
DBSCAN
Technology Article Subject Computer Networks and Communications Computer science 020206 networking & telecommunications 02 engineering and technology TK5101-6720 computer.software_genre ComputingMethodologies_PATTERNRECOGNITION 0202 electrical engineering electronic engineering information engineering Cluster (physics) Telecommunication Overhead (computing) 020201 artificial intelligence & image processing Data mining Electrical and Electronic Engineering Cluster analysis computer Information Systems |
| Zdroj: | Wireless Communications and Mobile Computing, Vol 2021 (2021) |
| ISSN: | 1530-8677 1530-8669 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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