A new iterative graph based clustering method

In this paper we propose a new iterative graph based clustering technique. The proposed method has three desirable characteristics as compared to well known clustering techniques. Specifically, because of its iterative nature, the number of clusters contained in a given data set it is not necessary to be known a priori as partitional clustering techniques demand. Its performance, in terms of the quality of the achieved clustering, does not depend on the distribution of the cluster´s size. Finally, no threshold value is required for achieving the clustering as hierarchical clustering techniques demand. All these features manifest themselves in the important problem of clustering homologous proteins when only sequence information is available. The proposed method is tested against well known and widely used techniques, by conducting a number of experiments based on both artificial and real protein data sets and all above mentioned characteristics were confirmed.