微型机与应用2012，Vol.31Issue(6)：76-79,4.
支持向量机优化基于K—means的蚁群聚类算法
SVM optimizing the K-means based antclust algorithm
莫锦萍 1张志刚2
作者信息
- 1. 广西财经学院现代教育技术部,广西南宁530003
- 2. 广西水利电力职业技术学院,广西南宁530024
- 折叠
摘要
Abstract
Firstly this paper proposes an improved AntClust algorithm （KM-AntClust）, which optimizes the rules of AntClust model with K-means mind, then vertifying the clustering effection by experiments. Secondly introducing SVM to turther improve the clustering effection, In this step, the SVM is trained with dataset beyond clusters center at frist, then gaining the global optimal clusters when SVM is utilized to reclassify the original datasets. Experimental results for UCI datasets demonstrate that the improved method can obviously improve the classification quality.关键词
K-平均算法/蚁群算法/聚类/支持向量机Key words
K-means/ant colony optimization/clustering/SVM分类
信息技术与安全科学引用本文复制引用
莫锦萍,张志刚..支持向量机优化基于K—means的蚁群聚类算法[J].微型机与应用,2012,31(6):76-79,4.
