| K-means聚类分析算法中一个新的确定聚类个数有效性的指标 |
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| 引用本文： | 李双虎,王铁洪. K-means聚类分析算法中一个新的确定聚类个数有效性的指标[J]. 河北省科学院学报, 2003, 20(4): 199-202 |
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| 作者姓名： | 李双虎 王铁洪 |
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| 作者单位： | 1. 河北省科学院应用数学研究所,石家庄,050081
2. 河北省科学院自动化研究所,石家庄,050081 |
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| 摘 要： | K-means 算法是聚类分析中使用最为广泛的算法之一.然而,该算法通常受到初始聚类条件的影响.关于这个问题的详细讨论可参看文献[1].该算法的另一个不足之处是,聚类数目K必须作为参数由用户提供.笔者提出了一个新的有关聚类有效性的度量指标和优化的K-means 算法.它能自动确定最佳聚类个数.
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| 关 键 词： | 聚类分析 K-means算法 有效性度量 指标 |
| 文章编号： | 1001-9383(2003)04-0199-04 |
| 修稿时间： | 2003-07-18 |
New validity index for determining the number of clusters in K-means clustering |
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| LI Shuang-hu. New validity index for determining the number of clusters in K-means clustering[J]. Journal of The Hebei Academy of Sciences, 2003, 20(4): 199-202 |
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| Authors: | LI Shuang-hu |
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| Affiliation: | LI Shuang-hu~ |
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| Abstract: | K-Means Clustering Algorithm is one of the most popular methods in cluster analysis. However, it is well known that K-means algorithm suffers from initial starting conditions effects(initial clustering and instance order effects). For more detailed discussion on initialization methods, see literature . Another weakness of k-means algorithm is that the number of clusters, k, must be supplied as a parameter. In this paper, a new validity measure for k-means clustering is presented to allow the number of clusters to be determined automatically. |
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| Keywords: | Cluster analysis K-Means Algorithm Validity measure Index |
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