Weighting variables in Kohonen competitive learning algorithms

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].

KEYWORDS:

• Kohonen competitive learning
• fuzzy KCL
• weighted KCL
• singular value decomposition

Acknowledgments

The authors would like to thank the anonymous reviewers for their constructive comments to improve the presentation of the paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is supported in part by the National Science Council, Taiwan, under Wen-LiangHung's Grants: MOST 101-2118-M-134-001.