Cooperative bare-bone particle swarm optimization for data clustering

被引:26
作者
Jiang, Bo [1 ]
Wang, Ning [1 ]
机构
[1] Zhejiang Univ, Natl Lab Ind Control Technol, Inst Cyber Syst & Control, Hangzhou 310027, Zhejiang, Peoples R China
基金
中国国家自然科学基金;
关键词
Data mining; Partitional clustering; Cooperative coevolution; Particle swarm optimization; DIFFERENTIAL EVOLUTION; COLONY APPROACH; ALGORITHM;
D O I
10.1007/s00500-013-1128-1
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Cooperative coevolution (CC) was used to improve the performance of evolutionary algorithms (EAs) on complex optimization problems in a divide-and-conquer way. In this paper, we show that the CC framework can be very helpful to improve the performance of particle swarm optimization (PSO) on clustering high-dimensional datasets. Based on CC framework, the original partitional clustering problem is first decomposed to several subproblems, each of which is then evolved by an optimizer independently. We employ a very simple but efficient optimization algorithm, namely bare-bone particle swarm optimization (BPSO), as the optimizer to solve each subproblem cooperatively. In addition, we design a new centroid-based encoding schema for each particle and apply the Chernoff bounds to decide a proper population size. The experimental results on synthetic and real-life datasets illustrate the effectiveness and efficiency of the BPSO and CC framework. The comparisons show the proposed algorithm significantly outperforms five EA-based clustering algorithms, i.e., PSO, SRPSO, ACO, ABC and DE, and K-means on most of the datasets.
引用
收藏
页码:1079 / 1091
页数:13
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