| 一种改进的K-均值聚类算法的研究 |
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| 引用本文： | 王圆妹.一种改进的K-均值聚类算法的研究[J].长江大学学报,2006,3(4):76-77. |
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| 作者姓名： | 王圆妹 |
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| 作者单位： | 长江大学电子信息学院,湖北,荆州,434023 |
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| 摘 要： | 聚类分析在科研和商业应用中都有着非常重要的作用。K-均值聚类算法是一种基于样本间相似性度量的间接聚类方法,其不足之处是,它采用均值作为一类的代表点,一个点往往不能充分反映该类的模式分布结构,从而损失了很多有用的信息。研究了一种改进的 K-均值聚类算法,在求样本间距离时,采用核函数距离代替欧氏距离,考虑了各模式间的相关性。试验结果表明,利用改进的 K-均值聚类算法,聚类结果的准确率更高,更稳定。
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| 关 键 词： | K-均值算法 相似度量 核函数 聚类 |
| 文章编号： | 1673-1409（2006）04-0076-02 |
| 收稿时间： | 2006-09-28 |
| 修稿时间： | 2006-09-28 |
On the Improvement of K-means Clustering Algorithm |
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| WANG Yuan-mei.On the Improvement of K-means Clustering Algorithm[J].Journal of Yangtze University,2006,3(4):76-77. |
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| Authors: | WANG Yuan-mei |
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| Abstract: | Clustering analysis plays an important role in scientific research and commercial applica- tion. The K-means algorithm is the indirect clustering algorithm based upon comparability measure- ment between points,but it has some faults,it uses means as representative point,the point can't fig- ure the distributional structure of the mode,thus some important information is missed. An improved K-means clustering algorithm is studied. Kernel function distance is used to replace Euclidean dis- tance. Experimental results show that clustering effect is higher veracity and more steady with im- provement of K-means clustering algorithm. |
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| Keywords: | K-means algorithm comparability measurement kernel function clustering |
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