A method and system for identifying and prioritizing business useful insights from hidden patterns. This invention relates to data mining techniques and more particularly to identify and prioritize insights from a plurality of insights present in a large set of data. Insight exploration is a method and system that enables the user to generate actionable insights, prioritize them for a given data. This falls broadly within the field of data mining. The primary achievement of this invention is to take a rule in if-then format and then systematically process them to identify actionable information from them. In that process, the system automatically prioritizes the rules, generates other rules and analyzes the path that leads to desired behavioral changes.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for analyzing insights generated for a data, the method comprising at least one of generating at least one generalized insight for at least one given rule from a set of generated insights by a data-analyzing engine; generating at least one specialized insight for at least one given rule from the set of generated insights by the data-analyzing engine; generating at least one customized insight for at least one given rule from the set of generated insights by the data-analyzing engine; and generating at least one what-if insight for a selected class level for at least one given rule from a set of generated insights by the data-analyzing engine. 2. The method, as claimed in claim 1, wherein generating at least one generalized insight for at least one given rule from a set of generated insights further comprises checking if number of antecedents in at least one generated insight is greater than 1 by the data-analyzing engine; dropping one attribute from the antecedent, if number of antecedents in at least one generated insight is greater than 1 by the data-analyzing engine; recomputing at least one metric by the data-analyzing engine; and checking by the data-analyzing engine, if support of a generated insight is greater than the support of the original insight and lift is greater than or equal to 1; saving the generated insight by the data-analyzing engine, if support of the at least one generated insight is greater than support of an original insight and lift is greater than or equal to 1; and resetting the generated insight to an original insight by the data-analyzing engine, if support of the at least one generated insight is greater than the support of the original insight and lift is greater than or equal to 1. 3. The method, as claimed in claim 1, wherein generating at least one specialized insight for at least one given rule from the set of generated insights further comprises checking number of attributes in an antecedent in at least one generated insight by the data-analyzing engine; generating all attribute-level combinations to the antecedent by adding the attribute to the antecedent by the data-analyzing engine; checking if confidence of the at least one generated insight is greater than confidence of an original insight and lift is greater than or equal to 1 by the data-analyzing engine; and saving the at least one generated insight by the data-analyzing engine, if confidence of the at least one generated insight is greater than the confidence of the original insight and lift is greater than or equal to 1. 4. The method, as claimed in claim 1, wherein generating at least one customized insight for at least one given rule from the set of generated insights further comprises checking number of attributes in an antecedent in at least one generated insight by the data-analyzing engine; obtaining at least one rule by changing at least one level of the attributes in the antecedent by the data-analyzing engine; computing at least one metrics by the data-analyzing engine for the at least one given rule with a same class level; checking if the at least one rule has at least one supported record, confidence in the at least one rule is greater than a minimum confidence and lift is greater than 1 by the data-analyzing engine; and considering the at least one valid rule as the customized insight by the data-analyzing engine, if the at least one rule has at least one supported record, confidence in the at least one rule is greater than the minimum confidence and the lift is greater than 1. 5. The method, as claimed in claim 1, wherein generating at least one what-if insight for a selected class level for at least one given rule from a set of generated insights further comprises checking if there is at least one actionable attribute by the data-analyzing engine, on receiving information comprising of at least one generated insight, at least one desired class level, and at least one actionable attribute; obtaining all possible rules by the data-analyzing engine by changing at least one level in the at least one actionable attribute; filtering the obtained rules by the data-analyzing engine for desired class in Right Hand Side (RHS); filtering the filtered rules by the data-analyzing engine for at least one rule with minimum support, minimum confidence and lift greater than 1; and displaying the filtered rules by the data-analyzing engine. 6. The method, as claimed in claim 5, wherein the method comprises of sorting the filtered rules based on at least one factor by the data-analyzing engine. 7. A system for analyzing insights generated for a data, the system configured for generating at least one generalized insight for at least one given rule from a set of generated insights; generating at least one specialized insight for at least one given rule from the set of generated insights; generating at least one customized insight for at least one given rule from the set of generated insights; and generating at least one what-if insight for a selected class level for at least one given rule from a set of generated insights. 8. The system, as claimed in claim 7, wherein the system is further configured for generating at least one generalized insight for at least one given rule from a set of generated insights by checking if number of antecedents in at least one generated insight is greater than 1; dropping one attribute from the antecedent, if number of antecedents in at least one generated insight is greater than 1; recomputing at least one metric; and checking if support of a generated insight is greater than the support of the original insight and lift is greater than or equal to 1; saving the generated insight, if support of the at least one generated insight is greater than support of an original insight and lift is greater than or equal to 1; and resetting the generated insight to an original insight, if support of the at least one generated insight is greater than the support of the original insight and lift is greater than or equal to 1. 9. The system, as claimed in claim 7, wherein the system is further configured for generating at least one specialized insight for at least one given rule from the set of generated insights by checking number of attributes in an antecedent in at least one generated insight; generating all attribute-level combinations to the antecedent by adding the attribute to the antecedent; checking if confidence of the at least one generated insight is greater than confidence of an original insight and lift is greater than or equal to 1; and saving the at least one generated insight, if confidence of the at least one generated insight is greater than the confidence of the original insight and lift is greater than or equal to 1. 10. The system, as claimed in claim 7, wherein the system is further configured for generating at least one customized insight for at least one given rule from the set of generated insights by checking number of attributes in an antecedent in at least one generated insight; obtaining at least one rule by changing at least one level of the attributes in the antecedent; computing at least one metrics for the at least one given rule with a same class level; checking if the at least one rule has at least one supported record, confidence in the at least one rule is greater than a minimum confidence and lift is greater than 1; and considering the at least one valid rule as the customized insight, if the at least one rule has at least one supported record, confidence in the at least one rule is greater than the minimum confidence and the lift is greater than 1. 11. The system, as claimed in claim 7, wherein the system is further configured for generating at least one what-if insight for a selected class level for at least one given rule from a set of generated insights by checking if there is at least one actionable attribute, on receiving information comprising of at least one generated insight, at least one desired class level, and at least one actionable attribute; obtaining all possible rules by changing at least one level in the at least one actionable attribute; filtering the obtained rules for desired class in Right Hand Side (RHS); filtering the filtered rules for at least one rule with minimum support, minimum confidence and lift greater than 1; and displaying the filtered rules. 12. The system, as claimed in claim 11, wherein the system is further configured for sorting the filtered rules based on at least one factor.