1. A method for evaluating and outputting a final clustering solution for a plurality of multi-dimensional data records, said data records having multiple, heterogeneous feature spaces represented by feature vectors, said method comprising: defining a distortion between two feature vectors as a weighted sum of distortion measures on components of said feature vector; clustering said multi-dimensional data records into k-clusters using a “convex programming” formulation; and selecting feature weights of said feature vectors.