计算机应用研究2017，Vol.34Issue(6)：1626-1630,5.DOI:10.3969/j.issn.1001-3695.2017.06.006
基于改进粒子群优化的无标记数据鲁棒聚类算法
Improved particle swarm optimization based robust clustering algorithm for unlabeled data
摘要
Abstract
Concerned at the problem that the most existing clustering algorithms only consider single object and they show poor performance in some datasets with particular shapes,this paper proposed an improved PSO based robust clustering algorithm for unlabeled data to resolve above problem.In the optimization phase,firstly,it adopted the classical formation of multi-objective PSO to generate the clustering solution set.Then,it adopted the K-means algorithm to generate the random distributed initial population,and assigned the random initial velocity to each particle.Lastly,it adopted the maximin strategy to decide the Pareto optimality.In the decision phase,it measured the distances between Pareto optimal solutions and ideal solution and selected the shortest one as the final clustering solution.Compared experimental results show that the proposed algorithm show better clustering performance to datasets with different shapes and is robust to the complexity of objective problems.关键词
多目标粒子群优化/聚类算法/鲁棒性/帕累托最优解/无标记数据Key words
multi-objective particle swarm optimization/clustering algorithm/robustness/Pareto optimality/unlabeled data分类
信息技术与安全科学引用本文复制引用
茹蓓,朱楠,贺新征..基于改进粒子群优化的无标记数据鲁棒聚类算法[J].计算机应用研究,2017,34(6):1626-1630,5.基金项目
河南省高等学校青年骨干教师培养计划资助项目(2013GGJS-222) （2013GGJS-222）
河南省科技厅资助项目(152400410345) （152400410345）
河南省教育厅资助项目(15A520093) （15A520093）
河南省科技厅科技攻关项目(172102210445) （172102210445）
