Weighting variables in Kohonen competitive learning algorithms

被引:0
|
作者
Hung, Wen-Liang [1 ]
Chen, De-Hua [2 ]
Yang, Jenn-Hwai [3 ]
机构
[1] Natl Hsinchu Univ Educ, Dept Appl Math, Hsinchu, Taiwan
[2] Zhejiang Ind & Trade Vocat Coll, Coll Informat & Commun, Wenzhou, Peoples R China
[3] Acad Sinica, Inst Biomed Sci, Taipei, Taiwan
关键词
Kohonen competitive learning; fuzzy KCL; weighted KCL; singular value decomposition; NEURO-FUZZY APPROACH; IMAGE SEGMENTATION; GENE-EXPRESSION; SELECTION;
D O I
10.1080/02664763.2016.1168367
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
This paper presents a new variable weight method, called the singular value decomposition (SVD) approach, for Kohonen competitive learning (KCL) algorithms based on the concept of Varshavsky et al. [18]. Integrating the weighted fuzzy c-means (FCM) algorithm with KCL, in this paper, we propose a weighted fuzzy KCL (WFKCL) algorithm. The goal of the proposed WFKCL algorithm is to reduce the clustering error rate when data contain some noise variables. Compared with the k-means, FCM and KCL with existing variable-weight methods, the proposed WFKCL algorithm with the proposed SVD's weight method provides a better clustering performance based on the error rate criterion. Furthermore, the complexity of the proposed SVD's approach is less than Pal et al. [17], Wang et al. [19] and Hung et al. [9].
引用
收藏
页码:212 / 232
页数:21
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