Weighting variables in Kohonen competitive learning algorithms

Wen-Liang Hung, De-Hua Chen and Jenn-Hwai Yang

Journal of Applied Statistics, 2017, vol. 44, issue 2, 212-232

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].

Date: 2017
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DOI: 10.1080/02664763.2016.1168367

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