Bezdek型模糊属性C均值聚类算法

刘敬伟; 徐美芝

Bezdek型模糊属性C均值聚类算法

刘敬伟¹,
徐美芝²

1.
北京航空航天大学理学院, 北京 100083
2. 清华大学数学科学系, 北京100084

基金项目: 中国博士后基金资助项目(2003033145)

详细信息

作者简介:
刘敬伟(1970-),男,山东昌邑人,副教授,jwliu@buaa.edu.cn.

中图分类号: O 235; O159; TP 391.41
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- 被引次数: 0
出版历程
- 收稿日期: 2006-08-23
- 网络出版日期: 2007-09-30

Bezdek type fuzzy attribute C-means clustering algorithm

Liu Jingwei¹,
Xu Meizhi²

1.
School of Science, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
2. Department of Mathematical Sciences, Tsinghua University, Beijing, 100084

摘要

摘要: 推广了属性均值聚类算法,提出了基于模糊度m的Bezdek型模糊属性C均值聚类算法(FAMC),给出了FAMC算法的迭代算法,并讨论了模糊度m对算法收敛性的影响.在标准Iris数据集与肿瘤基因芯片表达数据的模式识别实验结果,验证了该算法优于模糊C均值算法和属性均值聚类算法.
- 模糊C均值聚类算法 /
- 属性均值聚类 /
- 稳态函数 /
- 基因表达数据
Abstract: Bezdek type fuzzy attribute C-means clustering algorithm(FAMC) was proposed by extending attribute means clustering(AMC) algorithm based on fuzziness index (or weighting exponent) m. The iterative algorithm was derived and the effect of fuzziness index m on objective function convergence was discussed. The experimental results of pattern recognition performances on standard Iris database and tumor/normal gene chip expression data demonstrate that FAMC is more effective than fuzzy C-means clustering(FCM) algorithm and AMC.
- fuzzy C-means clustering /
- attribute means clustering /
- stable function /
- gene expression data

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参考文献(1)

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