1. A method for clustering documents comprising: assigning each of a set of data points to a respective cluster, each of the data points representing a respective document; with a computer processor, for a plurality of iterations: clustering the data points among the clusters, comprising assigning the data points to the clusters based on a comparison measure of each data point with a representative point of each cluster, and a threshold of the comparison measure, and for each of a plurality of the iterations, computing an overall score for the clustering, based on the assignments in the clustering of that iteration and the computed representative points for that iteration, wherein the score is computed according to: score ⁡ ( C , γ ) = ∑ c ∈ C ⁢ ∑ p ∈ γ ⁡ ( c ) ⁢ ( sim ⁡ ( p , repr ⁡ ( c ) ) - τ ) where c represents a cluster from the set of clusters C; p represents one of the data points; γ(c) represents the assignment of a subset of the data points to cluster c; sim(p,repr(c)) represents the cosine similarity between the data point p and cluster c's representative point; and τ represents the threshold; and based on the clustering, computing a new representative point for each of the clusters, which serves as the representative point for a subsequent iteration; and outputting an assignment of the documents to the clusters, based on the clustering of the data points in one of the iterations.