1. A computer-implemented method for ranking a target data record set, the method performed using at least one processor, comprising: generating a learned rule set from a training data record set, wherein each training record in the training data record set is pre-determined to belong to a class from a plurality of classes, and wherein a learned rule from the learned rule set having one or more rule conditions is associated with one or more training records from the training data record set having attribute values satisfying the one or more rule conditions; creating, for each rule in the learned rule set, at least one prototype representing the rule in a multi-dimensional feature space to thereby generate a prototype set wherein each said at least one prototype approximates a point in the multi-dimensional feature space corresponding to an average of values for each feature from training data records associated with the rule; and ranking the target data record set using at least the prototype set, wherein the ranking includes assigning a score to at least one target data record in the target data record set based upon a distance in the multi-dimensional feature space from the at least one target data record to at least one prototype from the prototype set.