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...

Full description

Saved in:
Bibliographic Details
Published in:PloS one 2017-12, Vol.12 (12), p.e0188815-e0188815
Main Authors: Gong, Congcong, Chen, Haisong, He, Weixiong, Zhang, Zhanliang
Format: Article
Language:English
Subjects:
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
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0188815