24. The method of claim 23 , wherein the iterative processing is repeated until a convergence criterion is reached, and then transforming the iteratively processed precursors of the cluster indicator matrices into cluster indicator matrices by use of the k means algorithm.
23. The method of claim 22 , wherein the precursors to the cluster indicator matrices are denoted {C (p) } 1≦p≦m ; the feature matrix product is denoted F (p) (F (p) ) T ; the relationship/cluster matrix product is a function of two or more terms selected from the group ((R (pj) C (j) (C (j) ) T (R (pj) T )) and ((R (jp) ) T C (j) (C (j) ) T (R (jp) )); j is an index variable running between p and m for the first term and between 1 and p for the second term, respectively; R (pj) ε n p ×n j is a relation matrix representing a relation between data object vectors χ p and χ j , and R (jp) ε n j ×n p is a relation matrix representing a relation between χ j and χ p ; χ j and χ p are members of m sets to be clustered into k j and k p disjoint clusters, respectively; and F (p) ε n p ×f j is a feature matrix where an element F pq (p) denotes the q th feature value for the object χ p , and f j is the number of features for χ i .