8. A computer readable storage medium storing executable instructions for controlling a programmable computer to combining multiple clusterings by performing the steps comprising: iteratively computing 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; and selecting an optimum consensus clustering which demonstrates a minimum distance, 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.
8. A computer readable storage medium storing executable instructions for controlling a programmable computer to combining multiple clusterings by performing the steps comprising: iteratively computing 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; and selecting an optimum consensus clustering which demonstrates a minimum distance, 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. 9. The computer readable storage medium according to claim 8 , wherein the objective function comprises determining a Euclidean distance.