A computer-implemented method for profiling a population of examples includes a computer system creating a rule collection comprising a plurality of rules, wherein each rule describes a respective corresponding sub-population of the examples according to a conjunction of a plurality of feature-value pairs. The computer system generates a precisely descriptive profile by performing a search process on the rule collection to identify a rule that either maximizes or minimizes the value of a user-specified target feature in the respective corresponding sub-population.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for profiling a population of examples, the method comprising: creating, by a computer system, a rule collection comprising a plurality of rules, wherein each rule describes a respective corresponding sub-population of the examples according to a conjunction of a plurality of feature-value pairs; and generating, by the computer system, a precisely descriptive profile by performing a search process on the rule collection to identify a rule that either maximizes or minimizes a value of a user-specified target feature in the respective corresponding sub-population. 2. The method of claim 1, wherein the search process is implemented using a beam search algorithm. 3. The method of claim 1, wherein the search process is implemented using a Monte Carlo search algorithm. 4. The method of claim 1, wherein the search process maximizes a utility measurement for each rule in the plurality of rules. 5. The method of claim 4, wherein the utility measurement is based on a deviation (above or below) of the user-specified target feature in the respective corresponding sub-population from the mean value of the user-specified target feature in the population of examples. 6. The method of claim 5, wherein the utility measurement is further based on a weighted function of the value corresponding to the user-specified target feature and a sub-population count proscribed by the rule. 7. The method of claim 4, wherein the utility measurement is the magnitude of the Z-score of the respective corresponding sub-population, implicitly defining a weighting between population count and a target feature deviation from the mean. 8. The method of claim 4, wherein the utility measurement includes a constraint selected from (i) a first constraint that the respective sub-population must include a minimum number of population members or (ii) a second constraint that the respective corresponding sub-population must comprise a minimum percentage of the population. 9. The method of claim 1 wherein the number of feature-value pairs in the plurality of rules is bounded by a user-specified parameter. 10. The method of claim 1, further comprising: prior to creating the rule collection, performing a pre-processing process on the population examples comprising: identifying a plurality of ordinal features included in the population of examples which correspond to the user-specified target feature; dividing the plurality of ordinal features into a plurality of bins according to corresponding feature values; and performing a condition creation process for each rule comprising: identifying a subset of the plurality of bins having a significant deviation from the mean value of the population with respect to the user-specified target feature, and combining ordinal features included in the subset of the plurality of bins. 11. The method of claim 10, wherein the pre-processing process further comprises: identifying a plurality of nominal features included in the population of examples; and during the condition creation process for each rule, combining the plurality of nominal features into disjunctive subsets of the population of examples. 12. The method of claim 1, wherein the method further comprises an iterative process comprising: removing a particular sub-population covered from by the precisely descriptive profile from an example collection; and repeating the search process on remaining examples in the example collection to generate a second precisely descriptive profile. 13. A system for profiling a population of examples, the system comprising: a database configured to store a rule collection comprising a plurality of rules, wherein each rule describes a respective corresponding sub-population of the examples according to a conjunction of a plurality of feature-value pairs; and a plurality of processors configured to generate a precisely descriptive profile by performing a search process on the rule collection to identify a rule that either maximizes or minimizes a value of a user-specified target feature in the respective corresponding sub-population. 14. A computer-implemented method for profiling a population of examples, the method comprising: receiving, by a computer system, a user-specified target feature; determining, by the computer system, a performance measurement for each example in the population with regards to the user-specified target feature; identifying, by the computer system, a sub-population of the examples based on the performance measurement determined for each example, wherein the sub-population comprises one of (i) highest performers with respect to the user-specified target feature or (ii) lowest performers with respect to the user-specified target feature; determining, by the computer system, a population mean value for the user-specified target feature across the population; identifying, by the computer system, feature-value pairs from the sub-population that deviate from the population mean value by more than a predetermined threshold value; and displaying the identified feature-value pairs. 15. The method of claim 14, further comprising: performing similarity-based clustering on the sub-population to generate a plurality of mutually exclusive sets; for each mutually exclusive set, determining a first deviation value indicative of a degree to which the mutually exclusive set deviates from the population mean value with respect to the user-specified target feature; and displaying the first deviation value associated with each of the plurality of mutually exclusive sets. 16. The method of claim 15, further comprising: for each mutually exclusive set in the plurality of mutually exclusive sets, determining a second deviation value indicative of a degree to which the mutually exclusive set deviates from other members of the plurality of mutually exclusive sets with respect to the user-specified target feature; and displaying the second deviation value associated with each of the plurality of mutually exclusive sets. 17. The method of claim 15, wherein the plurality of mutually exclusive sets are produced hierarchically on the sub-population. 18. The method of claim 15, wherein the similarity-based clustering produces a quasi-optimal number of mutually exclusive sets by an iterative process comprising: creating a new set; and successively adding clusters to the new set until the new set does not significantly differ from one or more prior sets. 19. The method of claim 15, wherein the computer system comprises a plurality of processors and the similarity-based clustering is performed in parallel. 20. The method of claim 14, wherein the computer system comprises a plurality of processors and each processor is configured to operate on a subset of the population in order to identify examples in the subset of the population meeting predetermined performance criteria. 21. The method of claim 14, wherein the computer system comprises a plurality of processors and each processor is configured to determine cohort deviation values over successive slices of the population in parallel. 22. The method of claim 14, further comprising: identifying, by the computer system, a plurality of cohorts in the population related to the user-specified target feature; identifying, by the computer system, additional feature-value pairs from the plurality of cohorts that deviate from the population mean value by more than the predetermined threshold value; and displaying the additional feature-value pairs. 23. A system for profiling a population of examples, the system comprising: a network interface configured to receive a user-specified target feature; a plurality of processors configured to: determine a performance measurement for each example in the population with regards to the user-specified target feature, identify a sub-population of the examples based on the performance measurement determined for each example, wherein the sub-population comprises one of (i) highest performers with respect to the user-specified target feature or (ii) lowest performers with respect to the user-specified target feature, determine a population mean value for the user-specified target feature across the population, and identify feature-value pairs from the sub-population that deviate from the population mean value by more than a predetermined threshold value; and a display configured to present the identified feature-value pairs.