| 基于SOFM网络的改进K-均值聚类算法 |
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| 引用本文： | 丁春荣,杨宝华.基于SOFM网络的改进K-均值聚类算法[J].科技导报(北京),2009,27(10). |
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| 作者姓名： | 丁春荣 杨宝华 |
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| 作者单位： | 安徽农业大学信息与计算机学院,合肥,230036
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| 基金项目： | 安徽省高校省级自然科学项目 |
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| 摘 要： | 针对传统的K-均值聚类算法中随机选取初始聚类中心的缺陷,提出一种改进的K-均值聚类算法,利用自组织特征映射网络(SOFM)自动获得初始聚类中心.实验结果表明,改进的K-均值聚类算法能有效改善聚类性能,提高聚类的准确率.
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| 关 键 词： | K-均值聚类 自组织特征映射网络 聚类中心 |
Improved K-Means Clustering Algorithm Based on SOFM |
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| DING Chunrong,YANG Baohua School of Information , Computer Science,Anhui Agricultural University,Hefei ,China.Improved K-Means Clustering Algorithm Based on SOFM[J].Science & Technology Review,2009,27(10). |
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| Authors: | DING Chunrong YANG Baohua School of Information Computer Science Anhui Agricultural University Hefei China |
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| Institution: | DING Chunrong,YANG Baohua School of Information , Computer Science,Anhui Agricultural University,Hefei 230036,China |
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| Abstract: | In view of the shortcomings of traditional K-means algorithm in not being able to select the initial clustering center automatically, a new improved K-means clustering algorithm is proposed, which obtains the initial clustering center by using Self- Organizing Feature Map (SOFM) automatically. Experimental results demonstrate that the improved K-means algorithm can improve the clustering performance effectively and enhance the clustering accuracy. |
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| Keywords: | K-means clustering self-organizing feature map clustering center |
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