9. A computer-implemented method, comprising: identifying a first data set and a second data set, establishing a plurality of relationships, each of the plurality of relationships linking an element of the first data set to an element of the second data set, identifying a set of categories for categorizing elements of the first and second data sets, initializing a plurality of first ranking vectors A, each of the first ranking vectors corresponding to an element of the first data set, each component of the each first ranking vector representing a degree to which the element is related to one of the categories, initializing a plurality of second ranking vectors U, each of the second ranking vectors corresponding to an element of the second data set, each component of the each second ranking vector representing a degree to which the element is related to one of the categories, assigning a link weight to each of the plurality of relationships of interconnected elements of the first and second data sets, said link weight being modifiable by interaction between the elements linked by the each relationship, wherein the ranking vectors are ranking vectors a and u, and each component a j of the at least one ranking vector a is incremented in an interaction by an amount proportional to a difference between components a j and u j of the ranking vectors a and u, the amount being computed from κ*(u j −a j ), κ being an interaction coefficient; computing a similarity quotient for the each relationship of the plurality of relationships, the similarity quotient being a measure of similarity between the elements linked by the each relationship and determined from the ranking vectors a and u, and classifying the plurality of relationships based on values of the similarity quotients.
11. The method of claim 9 , further comprising: applying a clustering operation to the plurality of relationships to generate clusters of similarity quotients, wherein the relationships are classified based on the clusters of similarity quotients.