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计算机系统应用:2020,29(3):213-217
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基于PCA优化的PSO-FCM聚类算法
(南华大学 计算机科学与技术学院, 衡阳 421001)
Optimized PSO-FCM Cluster Algorithm Based on Principal Component Analysis
(School of Computer Science and Technology, University of South China, Hengyang 421001, China)
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投稿时间:2019-07-29    修订日期:2019-09-05
中文摘要: 为解决PSO-FCM聚类算法针对多聚类问题,性能不足,容易陷入局部最优解,影响多聚类结果的准确度.提出一种基于PCA优化的PSO-FCM聚类算法,通过引入PCA分析方法,在粒子的各维度上设定不同的移动权重,降低粒子的敏感度,合理的控制粒子各维度上移动的速度,有效的降低粒子各维度上粒子无约束,位于多个聚类群交界处的粒子过分敏感,移动到错误的聚类的可能性增加.本文简要介绍了PSO-FCM算法的相关情况,详细介绍了本文的优化算法,最后通过实验证明,本文提出的优化算法在多个数据集上结果总体优于其他算法.
Abstract:For multi-cluster problems, PSO-FCM cluster algorithm is lack of performance and easily leads to local optimum, which affects the accuracy of multi-cluster result. To tackle these issues, an optimized PSO-FCM cluster algorithm based on PCA is put forward. By introducing PCA processing method, setting different movement weight on each dimension of particle and reducing particle sensitivity, reasonably controlling movement speed of particles on each dimension and effectively decreasing unconstrained particles on each dimension, possibility of moving into false cluster is increased due to over-sensitive particles on interface of multi-cluster groups. This paper introduces related conditions of PSO-FCM algorithm briefly and the proposed optimized algorithm in detail. Finally, this paper presents the experiment results, i.e., the optimized algorithm proposed in this study is totally better than other algorithms in many data sets.
文章编号:7342     中图分类号:    文献标志码:
基金项目:国家自然科学基金(61402220,61502221);湖南省哲学社会科学基金(16YBA323);湖南省自然科学基金(2015JJ3015);湖南省教育厅青年项目(15B207,18B279);南华大学科研创新项目(193YXC015)
引用文本:
陈诚,刘振宇.基于PCA优化的PSO-FCM聚类算法.计算机系统应用,2020,29(3):213-217
CHEN Cheng,LIU Zhen-Yu.Optimized PSO-FCM Cluster Algorithm Based on Principal Component Analysis.COMPUTER SYSTEMS APPLICATIONS,2020,29(3):213-217

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