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山东大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (2): 1-9.doi: 10.6040/j.issn.1672-3961.1.2014.095

• 机器学习与数据挖掘 •    下一篇

一种基于协同进化方法的聚类集成算法

董红斌, 张广江, 逄锦伟, 韩启龙   

  1. 哈尔滨工程大学计算机科学与技术学院, 黑龙江 哈尔滨 150001
  • 收稿日期:2014-03-26 修回日期:2015-03-17 出版日期:2015-04-20 发布日期:2014-03-26
  • 作者简介:董红斌(1963-),男,黑龙江哈尔滨人,教授,博士,主要研究方向为人工智能,协同演化算法,数据挖掘与机器学习.E-mail:donghongbin@hrbeu.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(60973075,61272186,61472095);哈尔滨工程大学中央高校基本科研业务费资助项目(HEUCFl00607)

A clustering ensemble algorithm based on co-evolution

DONG Hongbin, ZHANG Guangjiang, PANG Jinwei, HAN Qilong   

  1. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, Heilongjiang, China
  • Received:2014-03-26 Revised:2015-03-17 Online:2015-04-20 Published:2014-03-26

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摘要： 针对单一聚类算法存在的不能泛化的问题,将集成学习技术应用于聚类算法中,集成学习技术可以显著提高学习系统的泛化能力。提出了1种基于粒子群和遗传算法的协同进化聚类集成算法,粒子群算法保证算法快速收敛,遗传算法全局搜索扩大搜索范围,提高了聚类的性能和收敛速度。将本研究提出的算法在多个UCI数据集上进行试验验证,结果表明该算法是有效的。

关键词: 协同聚类集成, 粒子群优化算法, 协同进化, 聚类集成, 聚类, 遗传算法

Abstract: Since clustering could not solve the problem of generalization, the integration technology was introduced into clustering algorithm, which could significantly improve the generalization ability of learning systems. A co-evolutionary clustering ensemble algorithm based on particle swarm optimization and genetic algorithm (CEGPCE) was proposed. PSO (particle swarm optimization) ensured the algorithm with fast convergence, and GA (genetic algorithm) expanded the search scope with its global search capability, which improved the performance of the algorithm and the convergence speed. Experiments on the UCI data sets verified the effectiveness of CEGPCE.

Key words: particle swarm optimization, genetic algorithm, co-evolutionary clustering ensemble, clustering, clustering ensemble, co-evolution

中图分类号: 

  • TP18
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