Clustering based on rank distance with applications on DNA

Abstract

This paper aims to present two clustering methods based on rank distance. The K-means algorithm represents each cluster by a single mean vector. The mean vector is computed with respect to a distance measure. A new K-means algorithm based on rank distance is described in this paper. Hierarchical clustering builds models based on distance connectivity. Our paper introduces a new hierarchical clustering technique that uses rank distance. Experiments using mitochondrial DNA sequences extracted from several mammals demonstrate the clustering performance and the utility of the two algorithms. © 2012 Springer-Verlag.