1. A clustering method comprising: constructing a nonnegative sparse similarity matrix for a set of objects wherein the elements of the nonnegative sparse similarity matrix comprise similarity measures s(d i ,d j ) where {d i , d j } is a pair of objects of the set of objects and wherein the constructing includes performing a sparsification operation; performing nonnegative factorization of the nonnegative sparse similarity matrix wherein the performing comprises: approximating the nonnegative sparse similarity matrix by a product A×B of factor matrices A and B where the factor matrix A has R columns and the factor matrix B has R rows where R is a preselected number of clusters, and initializing parameters of the factor matrices A and B based on the preselected number of clusters R and a set of cluster centroids wherein the initializing comprises initializing parameters of the factor matrices A and B based on distances between nodes of the nonnegative sparse similarity matrix and the cluster centroids; and allocating objects of the set of objects to clusters based on factor matrices generated by the nonnegative factorization of the nonnegative sparse similarity matrix; wherein the constructing, performing, and allocating are performed by a processor executing software or firmware.