Asymmetric k-means clustering of the asymmetric self-organizing map

Abstract

In this paper, an asymmetric approach to clustering of the asymmetric Self-Organizing Map (SOM) is proposed. The clustering is performed using an improved asymmetric version of the well-known k-means algorithm. The improved asymmetric k-means algorithm is the second proposal of this paper. As a result, we obtain the two-stage fully-asymmetric data analysis technique. In this way, we maintain the structural consistency of the both utilized methods, because they are both formulated in asymmetric version, and consequently, they both properly adjust to asymmetric relationships in analyzed data. The results of our experiments confirm the effectiveness of the proposed approach. © 2014 Springer International Publishing.