A method and a system for interpreting a dataset comprising a plurality of items is described herein. The method may include computing a rule set pertaining to the dataset, generating a rule cover, calculating a plurality of distances between the plurality of rule pairs in the rule cover and generating a distance matrix based on the calculated plurality of distances between the plurality of rule pairs, storing the calculated plurality of distances between the plurality of rule pairs, clustering the overlapping rules within the rule cover using the distance matrix; selecting a representative rule from each cluster, determining at least one exception for each representative rule in the rule cover selected from each cluster and interpreting the dataset using the representative rules and the at least one exception determined for each representative rule in the rule set.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for interpreting a dataset comprising a plurality of items, the method comprising a processor (110) implemented steps of: computing a rule set pertaining to the dataset, wherein each rule within the rule set comprises a pre-determined consequent based on one or more antecedents, and generating a rule cover comprising a plurality of rules, wherein the rule cover pertains to a subset of the rule set using a rule generation module (120); calculating a plurality of distances between the plurality of rule pairs and generating a distance matrix based on the calculated plurality of distances between the plurality of rule pairs in the rule cover and storing the calculated plurality of distances between the plurality of rule pairs; clustering overlapping rules within the rule cover using the distance matrix, wherein the overlapping rules pertain to common set of transactions in the data; selecting a representative rule from each cluster, wherein the representative rule represents transactions covered by the rules contained within each cluster; determining at least one exception for each representative rule in the rule set selected from each cluster, wherein the at least one exception is determined when the one or more antecedents provide a result other than the pre-determined consequent; and interpreting the dataset using the representative rule and the at least one exception determined for each representative rule in the rule set using an interpretation module (122). 2. The method of claim 1, wherein the plurality of items pertain to a plurality of unique fields in a transactional database. 3. The method of claim 1, wherein computing the rule set pertaining to the dataset further comprises steps of identifying a plurality of frequently occurring itemsets in the dataset, wherein each of the plurality of frequently occurring itemset forms a rule within the rule set; and evaluating value of at least one parameter associated with the rule within the rule set using the rule generation module (120). 4. The method of claim 1, wherein the rule set computation is based on an association rule mining technique. 5. The method of claim 1, wherein the rule set is computed for a pre-determined consequent using the at least one parameter. 6. The method of claim 5, wherein the at least one parameter comprises a support of the rule, a confidence of the rule, and a lift of the rule. 7. The method of claim 3, wherein the plurality of frequently occurring itemsets in the dataset is determined by employing a frequent pattern mining technique. 8. The method of claim 1 wherein the rule cover is indicative of cumulative support of the rules in the dataset. 9. The method of claim 1, wherein the clustering comprises quantifying a degree of overlap between the overlapping rules. 10. The method of claim 1, wherein the representative rule is selected by a batch mode technique or an interactive mode technique. 11. The method of claim 1, the at least one exception is determined based on a pre-defined confidence threshold. 12. The method of claim 1, further comprises of storing the dataset in a database (108). 13. A data interpretation system (102) for interpreting a dataset having a plurality of items, the data interpretation system (102) comprising: a processor (110); a rule generation module (120), adapted for computing a rule set pertaining to the dataset, wherein each rule within the rule set comprises a pre-determined consequent based on one or more antecedents; generating a rule cover comprising a plurality of rules, wherein the rule cover pertain to a subset of the rule set; an interpretation module (122), adapted for calculating a plurality of distances between the plurality of rule pairs in the rule cover and generating a distance matrix based on the calculated plurality of distances between the plurality of rule pairs and storing the calculated plurality of distances between the plurality of rule pairs; clustering overlapping rules within the rule cover using the distance matrix, wherein the overlapping rules pertain to common set of transactions in the data; selecting a representative rule from each cluster, wherein the representative rule represents transactions covered by the rules contained within each cluster; determining at least one exception for each representative rule in the rule set selected from each cluster, wherein the at least one exception is determined when the one or more antecedents provide a result other than the pre-determined consequent; and interpretating the dataset using the representative rules and the at least one exception determined for each representative rule in the rule set. a database (108) adapted for storing the dataset. 14. The data interpretation system (102) of claim 13, wherein the rule generation module (120) is further adapted for identifying a plurality of frequently occurring itemsets in the dataset, wherein each of the plurality of frequently occurring itemset forms a rule within the rule set; and evaluating value of at least one parameter associated with the rule within the rule set. 15. A non-transitory computer-readable medium having embodied thereon a computer program for executing a method interpreting a dataset comprising a plurality of items, the method comprising: computing a rule set pertaining to the dataset, wherein each rule within the rule set comprises a pre-determined consequent based on one or more antecedents, and generating a rule cover comprising a plurality of rules, wherein the rule cover pertain to a subset of the rule set; calculating a plurality of distances between the plurality of rule pairs and generating a distance matrix based on the calculated plurality of distances between the plurality of rule pairs in the rule cover and storing the calculated plurality of distances between the plurality of rule pairs; clustering overlapping rules within the rule cover using the distance matrix, wherein the overlapping rules pertain to common set of transactions in the data; selecting a representative rule from each cluster, wherein the representative rule represents transactions covered by the rules contained within each cluster; determining at least one exception for each representative rule in the rule set selected from each cluster, wherein the at least one exception is determined when the one or more antecedents provide a result other than the pre-determined consequent; and interpreting the dataset using the representative rules and the at least one exception determined for each representative rule in the rule set.