一种改进的基于K-Means的蚁群聚类算法  

An Improved Ant Colony Clustering Algorithm Based on K-Means

作　　者：尚玉新[1] 

机构地区：[1]山东商业职业技术学院,山东济南250103

出　　处：《山东商业职业技术学院学报》2015年第1期93-95,共3页Journal of Shandong Institute of Commerce and Technology

摘　　要：聚类分析被广泛用于数据挖掘等领域,基于蚁群算法的聚类算法也得以应用。针对K-Means算法和蚁群聚类算法出现的缺点,利用了K-Means算法快速确定聚类中心和精英适应保留值的策略,提出了一种改进的基于K-Means的蚁群聚类算法。仿真实验表明,改进算法的性能得到有效提高。Clustering analysis is mainly used for dada mining, so does the ant colony clustering algorithm. In this paper, an improved ant colony algorithm is presented based on K - Means algorithm and a mechanism of the best solution kept. It can definite culster center fastly in the beginning and make the best solution kept for the next time. The simulated results show that the improved algorithm has better performance.

关 键 词：聚类分析 K-MEANS算法 蚁群算法 信息素 

分 类 号：P274[天文地球—测绘科学与技术]