| 初始聚类中心优化的k-means算法 |
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| 引用本文： | 袁方,周志勇,宋鑫.初始聚类中心优化的k-means算法[J].计算机工程,2007,33(3):65-66. |
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| 作者姓名： | 袁方 周志勇 宋鑫 |
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| 作者单位： | 河北大学数学与计算机学院,保定,071002 |
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| 基金项目： | 河北省科技厅攻关计划基金
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河北省教育厅科研项目 |
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| 摘 要： | 传统的k-means算法对初始聚类中心敏感，聚类结果随不同的初始输入而波动。为消除这种敏感性，提出一种优化初始聚类中心的方法，此方法计算每个数据对象所在区域的密度，选择相互距离最远的k个处于高密度区域的点作为初始聚类中心。实验表明改进后的k-means算法能产生质量较高的聚类结果，并且消除了对初始输入的敏感性。
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| 关 键 词： | 数据挖掘 聚类 k-means算法 聚类中心 |
| 文章编号： | 1000-3428（2007）03-0065-02 |
| 修稿时间： | 2006-04-27 |
K-means Clustering Algorithm with Meliorated Initial Center |
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| YUAN Fang,ZHOU Zhiyong,SONG Xin.K-means Clustering Algorithm with Meliorated Initial Center[J].Computer Engineering,2007,33(3):65-66. |
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| Authors: | YUAN Fang ZHOU Zhiyong SONG Xin |
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| Affiliation: | College of Mathematics and Computer, Hebei University, Baoding 071002 |
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| Abstract: | The traditional k-means algorithm has sensitivity to the initial start center.To solve this problem,a new method is proposed to find the initial start center.First it computes the density of the area where the data object belongs to;then finds k data objects all of which are belong to high density area and the most far away to each other,using these k data objects as the initial start centers.Experiments on the standard database UCI show that the proposed method can produce a high purity clustering result and eliminate the sensitivity to the initial start centers. |
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| Keywords: | Data mining Clustering K-means algorithm Clustering center |
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