6. A method for uncovering hidden structures in data embodied on a storage device, comprising executing operations implementing a spectral clustering algorithm on at least one automated computer, the operations comprising: characterizing clustering of data of at least first, second, and third types using first, second and third tentative cluster characterization matrices, respectively; and iteratively improving each tentative cluster characterization matrix using linear combinations of other matrices, the other matrices characterizing relationships between data of different types, wherein iteratively improving comprises calculating a matrix M (P) , which is a function of at least: at least one relation matrix representing respective relations between distinct members of m sets to be clustered into k p disjoint clusters, where p is an index running from 1 to m; at least one feature matrix where each element denotes a feature value for an associated data object; the tentative cluster characterization matrices; and a set of weights for different types of relations and features.
6. A method for uncovering hidden structures in data embodied on a storage device, comprising executing operations implementing a spectral clustering algorithm on at least one automated computer, the operations comprising: characterizing clustering of data of at least first, second, and third types using first, second and third tentative cluster characterization matrices, respectively; and iteratively improving each tentative cluster characterization matrix using linear combinations of other matrices, the other matrices characterizing relationships between data of different types, wherein iteratively improving comprises calculating a matrix M (P) , which is a function of at least: at least one relation matrix representing respective relations between distinct members of m sets to be clustered into k p disjoint clusters, where p is an index running from 1 to m; at least one feature matrix where each element denotes a feature value for an associated data object; the tentative cluster characterization matrices; and a set of weights for different types of relations and features. 8. The method of claim 6 , wherein the other matrices that are used to improve the first tentative cluster characterization matrix comprise at least one of the second and third tentative cluster characterization matrices.