测控技术2018，Vol.37Issue(5)：46-50,5.
基于自适应蚁群的FCM聚类优化算法研究
Optimization of Fuzzy C-Means Clustering Based on Adaptive Ant Colony Algorithm
摘要
Abstract
The fuzzy C-means (FCM) clustering algorithm needs to artificially set the number of clusters when initializing the algorithm,and randomly initializes the clustering center,which makes the FCM algorithm easily fall into the local optimal solution.The optimized FCM clustering algorithm was used to solve the above problems.The adaptive ant colony algorithm created the initial clustering center and the number of the centers,the data were used as the input of FCM clustering algorithm.The FCM algorithm and the optimized FCM algorithm were evaluated by clustering validity evaluation method of entropy and data geometric structure.Experimental results and analysis show the effectiveness of the method.关键词
模糊聚类/蚁群算法/优化模糊聚类/有效性指标Key words
FCM/ant colony algorithm/optimized FCM clustering algorithm/validation index分类
信息技术与安全科学引用本文复制引用
谈玲珑,汪青,李长凯..基于自适应蚁群的FCM聚类优化算法研究[J].测控技术,2018,37(5):46-50,5.基金项目
安徽新华学院校级科研项目(2017zr003) （2017zr003）
高校优秀青年骨干人才国内访学研修项目(gxgnfx2018070) （gxgnfx2018070）
