Weighting variables in Kohonen competitive learning algorithms. Issue 2 (25th January 2017)

• Record Type: Journal Article
• Authors: Hung, Wen-Liang; Chen, De-Hua; Yang, Jenn-Hwai
• Is Part Of: Journal of applied statistics. Volume 44:Issue 2(2017)
• Page Start: 212
• Page End: 232
• Publication Date: 2017-01-25
• Subjects: Kohonen competitive learning -- fuzzy KCL -- weighted KCL -- singular value decomposition; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2016.1168367
• Languages: English
• ISSNs: 0266-4763

Abstract

ABSTRACT: 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 ].