16. A system for ranking a target data record set, comprising: a processor; a rule learning module executed on the processor and configured to generate 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; a rule prototype generation module executed on the processor and configured to create a prototype set, wherein the prototype set comprising for each rule in the learned rule set, at least one prototype representing the rule in a multi-dimensional feature space, and 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 a scoring module executed on the processor and configured to rank 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.
16. A system for ranking a target data record set, comprising: a processor; a rule learning module executed on the processor and configured to generate 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; a rule prototype generation module executed on the processor and configured to create a prototype set, wherein the prototype set comprising for each rule in the learned rule set, at least one prototype representing the rule in a multi-dimensional feature space, and 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 a scoring module executed on the processor and configured to rank 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. 23. The system of claim 16 , wherein the ranking further includes: applying at least one rule from the learned rule set and at least one rule prototype from the prototype set to each data record from the target data record set, wherein a score is assigned to said each data record; and selecting a data record from the target data record set based on scores assigned to data record in said target data record set.