Research on adaptive entropy weight fuzzy c-means clustering algorithm

被引：0

作者：

Huang H. ^{[1
,2
]}

Kong C. ^{[1
]}

Yu H. ^{[2
]}

Wen F. ^{[1
]}

机构：

[1] School of Information Science and Engineering, Shenyang Ligong University, Shenyang

[2] Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang

来源：

Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | 2016年 / 36卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Adaptive; Entropy; Feature weighting; Fuzzy C-means clustering;

D O I：

10.12011/1000-6788(2016)01-0219-05

中图分类号：

学科分类号：

摘要：

Feature weight algorithm has great impact on the classification results. Traditional algorithms didn't consider distribution information among and inside classes. Therefore, study the impact of ordering degree of feature attributes after clustering, and analyse the distribution of feature attributes, named as adaptive feature entropy weight fuzzy C-means clustering algorithm (AEWFCM), is proposed. Both the clustering features entropy and the information gain are the criteria to adjust feature weights. By clustering iterative optimization weight gradually and continuously updated until the best feature weights obtained. Experimental results show that the AEWFCM algorithm can effectively distinguish the features attributes on the importance of clustering results; and compared with other famous fuzzy C-means clustering algorithms, it can get a higher accuracy in clustering with the same sample. © 2016, Systems Engineering Society of China. All right reserved.

引用

页码：219 / 223

页数：4

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