1. A method of determining a consensus clustering and correspondence matrices from a plurality of membership matrices, comprising: iteratively computing, with an automated processor, from r membership matrices M (1) ∈ n×k1 ,. .. , M (r) ∈ n×kr , k∈ + , the consensus clustering represented by M∈ n×k and the r correspondence matrices S (1) ∈ k×k ,. .. , S (h) ∈ kh×k , by minimization of an objective function comprising a consensus function f, updating S (h) using update rule: S ( h ) ← S ( h ) - Θ ⊙ ( ∂ f ∂ S ( h ) ) until predetermined convergence conditions are achieved, wherein ⊙ denotes the Hadamard product of two matrices, Θ is a matrix of step size parameters, and updating M using update rule: M ← M = 1 r ⁢ ∑ h = 1 r ⁢ M ( h ) ⁢ S ( h ) until predetermined convergence conditions are achieved; and storing the computed consensus clustering and the correspondence matrices in a memory.