| 支持向量机优化基于K-means的蚁群聚类算法 |
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| 引用本文： | 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. |
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| 作者姓名： | 莫锦萍 张志刚 |
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| 作者单位： | 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 |
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| 摘 要： | 基于K-means算法思想改进蚁群聚类算法聚类规则,提出一种新的K-means蚁群聚类算法,并通过实验验证其聚类效果;引入具有全局最优性的支持向量机SVM,取各类中心附近适当数据训练支持向量机,然后利用已获模型对整个数据集进行重新分类,进一步优化聚类结果,使聚类结果达到全局最优。UCI数据集实验结果表明,新的算法可以明显提高聚类质量。
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| 关 键 词： | K-平均算法 蚁群算法 聚类 支持向量机 |
SVM optimizing the K-means based antclust algorithm |
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| Mo Jinping,Zhang Zhigang.SVM optimizing the K-means based antclust algorithm[J].Microcomputer & its Applications,2012,31(6):76-79. |
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| Authors: | Mo Jinping Zhang Zhigang |
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| Affiliation: | 1.Modern Education Technology Center,Guangxi University of Finance and Economics,Nanning 530003,China;2.Guangxi Hydraulic and Electric Polytichnic,Nanning 530024,China) |
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| 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 further 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. |
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| Keywords: | K-means ant colony optimization clustering SVM |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
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