Fuzzy C-means Clustering Based on the Multivariable Weighted Attributes

LI Shi-jin; LI Qian; XU Gui-qiong

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Fuzzy C-means Clustering Based on the Multivariable Weighted Attributes

更新时间：2026-09-04

- Fuzzy C-means Clustering Based on the Multivariable Weighted Attributes
- Vol. 34, Issue 7, Pages: 92-95(2017)
- 作者机构：
  
  上海大学管理学院,上海,200444
- 作者简介：
- 基金信息：
- DOI：
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- Published：2017
- 稿件说明：
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李诗瑾, 李倩, 徐桂琼. 多属性加权的模糊 c 均值聚类算法[J]. 微电子学与计算机, 2017, 34(7): 92-95,100.

LI Shi-jin, LI Qian, XU Gui-qiong. Fuzzy C-means Clustering Based on the Multivariable Weighted Attributes[J]. 2017, 34(7): 92-95.
李诗瑾, 李倩, 徐桂琼. 多属性加权的模糊 c 均值聚类算法[J]. 微电子学与计算机, 2017, 34(7): 92-95,100. DOI：

LI Shi-jin, LI Qian, XU Gui-qiong. Fuzzy C-means Clustering Based on the Multivariable Weighted Attributes[J]. 2017, 34(7): 92-95. DOI：

摘要

在多属性模糊c均值聚类的基础上

提出了一种基于属性重要性加权的聚类算法.为验证新算法的有效性

在6个UCI数据集上

将新算法与结合主成分分析法和基于粗糙集指数加权的聚类方法进行了比较分析.实验结果表明

基于属性重要性的聚类算法具有更好的泛用性和稳定性

且随着平均类间簇心距离的增大而提升聚类有效性.

Abstract

Considering the difference of feature attributes

a new clustering algorithm based on the importance of attributes is proposed according to multivariable fuzzy c-means clustering method.In order to verify the validity of new method

the proposed algorithm is analyzed and compared with principal component comprehensive analysis and attributes weighted by rough set theory on six UCI datasets.The experimental results showed that this method has wider scope of application and stability.Moreover

the clustering efficiency increase when datasets have further average distance among cluster centers.

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Keywords

references

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邢婷, 邢治国, 王凤领. 基于信息熵的FCM聚类算法[J]. 计算机工程与设计, 2010, 31 (23): 5092-5095. DOI: 5092

李振东, 李飞. 基于粗糙集指数加权的聚类算法[J]. 统计与决策, 2015, 31 (2): 83-85. DOI: 83

肖林云, 陈秀宏, 林喜兰. 特征加权和优化划分的模糊C均值聚类算法[J]. 微电子学与计算机, 2016, 33 (10): 143-146. DOI: 143

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