Asymmetric $$k$$ -Means Clustering of the Asymmetric Self-Organizing Map.

An asymmetric approach to clustering of the asymmetric self-organizing map 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 a two-stage fully asymmetric data analysis technique. In this way, we maintain the methodological consistency of the both utilized methods, because they are both formulated in asymmetric versions, and consequently, they both properly adjust to asymmetric relationships in analyzed data. The results of our experiments on real data confirm the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]