4. The computer-implemented method from claim 1 , wherein said candidate conditional functional dependencies in said relevant set have a corresponding set of combined result signatures that have the best goodness of fit in terms of a maximum degree of coverage of said data set and a minimum proximity between a detected error rate and said predetermined error estimate.
4. The computer-implemented method from claim 1 , wherein said candidate conditional functional dependencies in said relevant set have a corresponding set of combined result signatures that have the best goodness of fit in terms of a maximum degree of coverage of said data set and a minimum proximity between a detected error rate and said predetermined error estimate. 5. The computer-implemented method from claim 4 , further comprising: ranking said candidate conditional functional dependencies in said relevant set according to an interestingness factor; wherein said interestingness factor increases for a particular one of said candidate conditional functional dependencies as a portion of said data set consisting of a data value on which said particular one of said candidate conditional functional dependencies is based on decreases.