结合蚂蚁算法的K-Means聚类分析

The K-Means Clustering Analysis Combined with Ant Colony

蚂蚁算法是一种新的基于种群的模拟进化算法,K-Means、基于密度的聚类是常见的基于分割的聚类方法,本文将蚂蚁算法、K-Means算法、密度思想结合在一起,提出了一种基于密度蚂蚁思想的K-Means算法,它利用蚂蚁算法的随机性,很大程度上解决局部最优问题,而且克服了K-Means算法初始参数的敏感性,提高了聚类的质量.再结合密度思想,使蚂蚁有选择地遍历,提高了算法效率,并克服了基于密度的算法不能发现任意形状聚类的问题.

The ant algorithm is a new evolutional method, k：means and the density-cluster are familiar cluster analysis, In this paper, we proposed a new K-Means algorithm based on density and ant theory, which resolved the problem of local minimal by the random city of ants and hurdled the original parameter sensitivity of k-means. It combined thought of density and made the ants searching select. It improved the efficiency and overcame the problem of not finding clustering center of arbitrariness shape.