| 基于最大树法和模糊C-均值算法的聚类分析 |
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| 引用本文： | 刘小芳,吕炳朝. 基于最大树法和模糊C-均值算法的聚类分析[J]. 四川理工学院学报(自然科学版), 2003, 16(4): 7-10 |
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| 作者姓名： | 刘小芳 吕炳朝 |
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| 作者单位： | 1. 四川轻化工学院计算机科学系,四川,自贡,643033
2. 电子科技大学,四川,成都,610054 |
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| 摘 要： | 对工程上常用的最大树法和模糊C-均值算法的聚类结果进行比较,从算法本身角度分析了其聚类结果的相似和不同之处。通过仿真验证:最大树法比较适合于低维的小样本集;模糊C-均值算法不仅适合于低维的小样本集,而且也适用于团状的、每类样本数相差不大的、类与类间有交叠的高维大样本集,更便于计算机上编程实现。
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| 关 键 词： | 模糊C-均值算法 最大树法 聚类分析 |
| 文章编号： | 1008-438X(2003)04-0007-04 |
| 修稿时间： | 2003-10-30 |
Clustering Analysis of Maximal Tree Method and Fuzzy C-Means Algorithm |
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| LIU Xiao-fang,LV Bing-chao. Clustering Analysis of Maximal Tree Method and Fuzzy C-Means Algorithm[J]. Journal of Sichuan University of Science & Engineering(Natural Science Editton), 2003, 16(4): 7-10 |
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| Authors: | LIU Xiao-fang LV Bing-chao |
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| Abstract: | Clustering results of fuzzy c-means algorithm and maximal tree method in common use of project are compared, similitude and difference of their clustering results are analyzed from algorithms' own angle. It is testified through simulation that maximal tree method is comparatively capable of small sample sets; and fuzzy c-means algorithms is not only capable of small sample sets, but also capable of multidimensional large sample sets of corps shape and discrepancy not too in sample number of every class and overlapping data among classes, still convenient for computer program realizing. |
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| Keywords: | fuzzy c-means algorithm clustering analysis maximal tree method |
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