A Novel Fuzzy Clustering Algorithm Based on Similarity of Attribute Space

被引：0
|
作者：
Shi W. [1 ]
Zhuo J. [1 ]
Lan Y. [1 ]
机构：
[1] Shanghai Maritime University, Shanghai
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2019年 / 41卷 / 11期
基金：
中国国家自然科学基金;
关键词：
Attribute similarity; Attribute topology subspace; Clustering reliability; Fuzzy C-Means (FCM) clustering; Principle of maximum attribute similarity;
D O I：
10.11999/JEITdzyxxxb-41-11-2722
中图分类号：
学科分类号：
摘要：
With the attribute feature information of the fuzzy membership matrix and cluster centers after the iteration not fully utilized, the results of Fuzzy C-Means (FCM) Clustering and related modified algorithms are determined based on the principle of maximum fuzzy membership, causing bad influence on the clustering accuracy. To solve this problem, the improvement ideas are proposed: to improve classification principle of FCM. The formula definition of attribute similarity in binary topological subspaces is given. Then, the improved FCM algorithm based on the Similarity of Attribute Space (FCM-SAS) is proposed: First, samples with fuzzy membership degree lower than the clustering reliability are selected as suspicious samples. Next, the attribute similarity between the suspicious samples and the cluster centers after clustering are calculated. Finally, cluster labels of suspicious samples based on the principle of maximum attribute similarity are updated. The validity and superiority of the proposed algorithm is verified by the UCI sample set experiments and comparisons with other modified algorithms based on the principle of maximum fuzzy membership. © 2019, Science Press. All right reserved.
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页码：2722 / 2728
页数：6
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