1. A system that identifies anomalous data in large structured data sets, the system including: a computer including memory; computer instructions causing the computer to implement: automatically expanding an existing tuple set of elements with features from a record set by adding one more feature to the existing tuple set and creating unique elements with the one more feature, wherein the unique elements in an expanded tuple set enumerate permutations of unique values of the features from the record set that are combined in the expanded tuple set; limiting unique elements in the expanded tuple set to inhabited feature value combinations by applying a threshold count criterion of 2 or more to counts of how often the feature value combinations of the unique elements are found in the record set and not retaining unique elements in the expanded tuple set that do not satisfy the threshold count criterion; after expanding the existing tuple set into the expanded tuple set and applying the threshold count criterion, comparing frequencies of the unique elements in the expanded tuple set to frequencies of the unique elements in a reference data set; and spotting outliers based on the comparing of the frequencies.