Described are embodiments for adaptive classification of data items which may include receiving a classification training set, the classification training set comprising a set of items associated with classification events made by a group of selected users, each item in the set of items having been designated as belonging to a particular classification by a selected user while manipulating the each item; determining from the classification training set a set of rules which can be used to classify unknown data items such that the classification of the unknown data items is consistent with the manual or automatic classification of the classification training set; adaptively updating the set of rules, according to classifications made to additional data items by additional users; and automatically classifying, based on the set of rules, one or more data items that are manipulated by a second set of one or more users.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for adaptive classification of data items in an enterprise, the method comprising: receiving a classification training set, the classification training set comprising a set of items associated with manual or automatic classification events made by a group of selected users, each item in the set of items having been designated as belonging to a particular classification by a selected user while manipulating the each item; determining from the classification training set a set of rules which can be used to classify unknown data items such that the classification of the unknown data items is consistent with the manual or automatic classification of the classification training set; adaptively updating the set of rules, according to classifications made to additional data items by additional users; and automatically classifying, based on the set of rules, one or more data items that are manipulated by a second set of one or more users. 2. A method according to claim 1, wherein the automatic classification is made, based at least in part on mutual patterns identified from the training set and items to be classified. 3. A method according to claim 2, wherein the patterns include one or more of: a level of similarity of each data item to other data items that already have been classified; a similar source and/or destination for transmitted data; a source of a data item; data item content; layout or structure of data within a data item; geo location; a group to which a data user belongs; data item metadata; templates that are reused by common users; and a level of similarity of fields and metadata between classified and unknown data items. 4. A method according to claim 1, wherein the automatic classification is made, based on one or more of: a shared storage location; and content analysis. 5. A method according to claim 1, further comprising receiving an overrule of an automatic classification, such that the overruled classification is a basis for additional updating of the set of rules. 6. A method according to claim 1, further comprising incorporating an initial knowledge base into the determination of the set of rules. 7. A method according to claim 1, further comprising: determining a reputation value for a classifying user; and assigning a weight to a classification based on the reputation value of the classifying user. 8. A computer system for adaptive classification of data items in an enterprise, the system comprising one or more computer processors and data storage having encoded therein computer-executable instructions which, when executed upon the one or more processors, cause the system to perform a method comprising: receiving a classification training set, the classification training set comprising a set of items associated with manual or automatic classification events made by a group of selected users, each item in the set of items having been designated as belonging to a particular classification by a selected user while manipulating the each item; determining from the classification training set a set of rules which can be used to classify unknown data items such that the classification of the unknown data items is consistent with the manual or automatic classification of the classification training set; adaptively updating the set of rules, according to classifications made to additional data items by additional users; and automatically classifying, based on the set of rules, one or more data items that are manipulated by a second set of one or more users. 9. A system according to claim 8, wherein the automatic classification is made, based at least in part on mutual patterns identified from the training set and items to be classified. 10. A system according to claim 9, wherein the patterns include one or more of: a level of similarity of each data item to other data items that already have been classified; a similar source and/or destination for transmitted data; templates that are reused by common users; and a level of similarity of fields and metadata between classified and unknown data items. 11. A system according to claim 8, wherein the automatic classification is made, based on one or more of: a shared storage location; and content analysis. 12. A system according to claim 8, further comprising receiving an overrule of an automatic classification, such that the overruled classification is a basis for additional updating of the set of rules. 13. A system according to claim 8, further comprising incorporating an initial knowledge base into the determination of the set of rules. 14. A system according to claim 8, further comprising: determining a reputation value for a classifying user; and assigning a weight to a classification based on the reputation value of the classifying user. 15. A computer program product for enabling the adaptive classification of data items in an enterprise, the computer program product comprising one or more data storage devices having encoded therein computer-executable instructions which, when executed upon one or more computer processors, cause the processors to be configured to perform a method comprising: receiving a classification training set, the classification training set comprising a set of items associated with manual or automatic classification events made by a group of selected users, each item in the set of items having been designated as belonging to a particular classification by a selected user while manipulating the each item; determining from the classification training set a set of rules which can be used to classify unknown data items such that the classification of the unknown data items is consistent with the manual or automatic classification of the classification training set; adaptively updating the set of rules, according to classifications made to additional data items by additional users; and automatically classifying, based on the set of rules, one or more data items that are manipulated by a second set of one or more users. 16. A computer program product according to claim 15, wherein the automatic classification is made, based at least in part on mutual patterns identified from the training set and items to be classified. 17. A computer program product according to claim 16, wherein the patterns include one or more of: a level of similarity of each data item to other data items that already have been classified; a similar source and/or destination for transmitted data; templates that are reused by common users; and a level of similarity of fields and metadata between classified and unknown data items. 18. A computer program product according to claim 15, wherein the automatic classification is made, based on one or more of: a shared storage location; and content analysis. 19. A computer program product according to claim 15, further comprising receiving an overrule of an automatic classification, such that the overruled classification is a basis for additional updating of the set of rules. 20. A computer program product according to claim 15, further comprising incorporating an initial knowledge base into the determination of the set of rules.