Improved multi-objective clustering algorithm using particle swarm optimization
Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO) is proposed. Firstly, a novel particle representation for clusteri...
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| Published in: | PloS one Vol. 12; no. 12; p. e0188815 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Public Library of Science
05.12.2017
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| ISSN: | 1932-6203, 1932-6203 |
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| Abstract | Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO) is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI. |
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| AbstractList | Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO) is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI. Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO) is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI.Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO) is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI. |
| Audience | Academic |
| Author | Gong, Congcong He, Weixiong Zhang, Zhanliang Chen, Haisong |
| AuthorAffiliation | Beihang University, CHINA PLA University of Science and Technology, Nanjing, PR China |
| AuthorAffiliation_xml | – name: Beihang University, CHINA – name: PLA University of Science and Technology, Nanjing, PR China |
| Author_xml | – sequence: 1 givenname: Congcong surname: Gong fullname: Gong, Congcong – sequence: 2 givenname: Haisong surname: Chen fullname: Chen, Haisong – sequence: 3 givenname: Weixiong surname: He fullname: He, Weixiong – sequence: 4 givenname: Zhanliang surname: Zhang fullname: Zhang, Zhanliang |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29206880$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Algorithms Analysis Biology and Life Sciences Cluster analysis Clustering Computer and Information Sciences Data analysis Datasets Earth Sciences Fuzzy sets Information science Intelligence International conferences Medicine and Health Sciences Methods Multiple objective analysis Optimization Optimization theory Particle swarm optimization Physical Sciences Problems Research and Analysis Methods Researchers Social Sciences Studies Swarm intelligence |
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| Title | Improved multi-objective clustering algorithm using particle swarm optimization |
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