1. A method for combining multiple clusterings comprising the steps of: iteratively computing, using at least one computer, a consensus clustering and correspondence matrices using multiplicative updating rules, to determine a distance between a target matrix and a transformed membership matrix in accordance with an objective function; automatically selecting, using the at least one automated processor, an optimum consensus clustering which demonstrates a minimum distance; and automatically generating a signal selectively in dependence on at least said automatically selecting, wherein the consensus clustering and correspondence matrices are iteratively computed from: 
 S (h) ←S (h) ⊙(( M (h) ) T M+βk 1 khk )/( D +ε), until a matrix convergence criterion is satisfied, wherein: ⊙ denotes the Hadamard product of two matrices, M denotes a membership matrix of a target clustering, M (h) denotes a membership matrix of a source clustering, S (h) denotes a correspondence matrix of M (h) with respect to M, β is a scaling constant ≧0, for scaling a penalty term added to a consensus function f(M,S (1) ,S (2) , . . . , S (r) ) to deal with an external constraint Σ j S (h) ij =1 efficiently, k denotes the number of clusters, 
 D= ( M (h) ) T M (h) S (h) −αS (h) +(α/ k h )1 khkh S (h) +βkS (h) 1 kk 1 khkh denotes a k h -by-k h matrix of 1s, α is a constant ≧0, selected to enforce a column-sparseness constraint, and ε is a very small positive number used to avoid dividing by 0.
1. A method for combining multiple clusterings comprising the steps of: iteratively computing, using at least one computer, a consensus clustering and correspondence matrices using multiplicative updating rules, to determine a distance between a target matrix and a transformed membership matrix in accordance with an objective function; automatically selecting, using the at least one automated processor, an optimum consensus clustering which demonstrates a minimum distance; and automatically generating a signal selectively in dependence on at least said automatically selecting, wherein the consensus clustering and correspondence matrices are iteratively computed from: 
 S (h) ←S (h) ⊙(( M (h) ) T M+βk 1 khk )/( D +ε), until a matrix convergence criterion is satisfied, wherein: ⊙ denotes the Hadamard product of two matrices, M denotes a membership matrix of a target clustering, M (h) denotes a membership matrix of a source clustering, S (h) denotes a correspondence matrix of M (h) with respect to M, β is a scaling constant ≧0, for scaling a penalty term added to a consensus function f(M,S (1) ,S (2) , . . . , S (r) ) to deal with an external constraint Σ j S (h) ij =1 efficiently, k denotes the number of clusters, 
 D= ( M (h) ) T M (h) S (h) −αS (h) +(α/ k h )1 khkh S (h) +βkS (h) 1 kk 1 khkh denotes a k h -by-k h matrix of 1s, α is a constant ≧0, selected to enforce a column-sparseness constraint, and ε is a very small positive number used to avoid dividing by 0. 5. The method according to claim 1 , wherein the multiple clusterings comprise clusterings of content anchor text, uniform resource locators (URLs), and hyperlinks of hyperlinked documents.