k-means clustering of asymmetric data

Abstract

In this paper, an asymmetric k-means clustering algorithm is presented. The asymmetric version of this algorithm is derived using the asymmetric coefficients, which convey the information provided by the asymmetry in analyzed data sets. The formulation of the asymmetric k-means algorithm is motivated by the fact that, when an analyzed data set has the asymmetric nature, a data analysis algorithm should properly adjust to this nature. The traditional k-means approach using the symmetric dissimilarities does not apply correctly to this kind of phenomenon in data. We propose the k-means algorithm using the asymmetric coefficients, which has the ability to reflect the asymmetric relationships between objects in analyzed data sets. The results of our experimental study on real data show that the asymmetric k-means approach outperforms its symmetric counterpart. © 2012 Springer-Verlag.