A system and method for providing document classification suggestions is provided. Clusters of uncoded documents are obtained. One or more of the uncoded documents in one such cluster are compared to a set of reference documents. Each reference document is assigned with a classification code. Those reference documents that are similar to the one or more uncoded documents are identified. Different types of the classification codes for at least a portion of the similar reference documents are identified and a count of the classification codes assigned to the portion of similar reference documents for each classification code type is obtained. A suggestion for classification of at least one of the one or more uncoded documents is provided based on the count of classification codes for each classification type and one of a presence and absence of each classification code type.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for providing document classification suggestions, comprising: a database to store clusters of uncoded documents; and a server comprising a central processing unit, memory, an input port to receive the clusters from the database, and an output port, wherein the central processing unit is configured to: compare one or more of the uncoded documents in one such cluster to a set of reference documents each assigned with a classification code; identify those reference documents in the set that are similar to the one or more uncoded documents; identify different types of the classification codes for at least a portion of the similar reference documents and obtaining a count of the classification codes assigned to the portion of similar reference documents for each classification code type; and provide a suggestion for classification of at least one of the one or more uncoded documents based on the count of classification codes for each classification type and one of a presence and absence of each classification code type. 2. A system according to claim 1, further comprising: a selection module to select the classification type that is present and has a highest count of classification codes as the suggested classification. 3. A system according to claim 1, further comprising: a cluster classification module to classify the cluster of the one or more uncoded documents, comprising: a counter module to obtain a count of classification codes for each of the classification types based on the similar reference documents and the uncoded documents with assigned classification codes in the cluster; and an assignment module to assign to the cluster the classification code of the classification type with a highest count of classification codes in the cluster. 4. A system according to claim 1, further comprising: a display to provide the clusters along one or more spines based on a similarity of the uncoded documents. 5. A system according to claim 4, further comprising: a determination module to determine the spine on which the cluster is placed; and a label module to identify a label associated with the identified spine, wherein the label is considered with the count of classification codes for each classification type and one of a presence and absence of each classification code type for the assignment of the classification code. 6. A system according to claim 4, further comprising: a document identification module to identify the similar reference documents, comprising: a determination module to determine a measure for the spine on which the cluster is placed; a comparison module to compare the measure with each of the reference documents; and a selection module to select the reference documents most similar to the measure as the similar reference documents. 7. A system according to claim 1, further comprising: a confidence module to determine a confidence level of the suggested classification and to provide the confidence level with the suggested classification. 8. A system according to claim 1, further comprising: a document identification module to identify the similar reference documents, comprising: a determination module to determine a measure for the one or more uncoded documents in the cluster, wherein the measure comprises one of a center of the cluster, a sample of the uncoded documents, and the cluster center and the sample; a comparison module to compare the measure with each of the reference documents; and a selection module to select the reference documents most similar to the measure as the similar reference documents. 9. A system according to claim 1, wherein the similar reference documents satisfy a threshold number. 10. A system according to claim 1, further comprising: a receipt module to receive from a user, a location for injecting the similar reference documents into the cluster. 11. A method for providing document classification suggestions, comprising: obtaining clusters of uncoded documents; comparing one or more of the uncoded documents in one such cluster to a set of reference documents each assigned with a classification code; identifying those reference documents in the set that are similar to the one or more uncoded documents; identifying different types of the classification codes for at least a portion of the similar reference documents and obtaining a count of the classification codes assigned to the portion of similar reference documents for each classification code type; and providing a suggestion for classification of at least one of the one or more uncoded documents based on the count of classification codes for each classification type and one of a presence and absence of each classification code type. 12. A method according to claim 11, further comprising: selecting the classification type that is present and has a highest count of classification codes as the suggested classification. 13. A method according to claim 11, further comprising: classifying the cluster with the one or more uncoded documents, comprising: obtaining a count of classification codes for each of the classification types based on the similar reference documents and the uncoded documents with assigned classification codes in the cluster; and assigning to the cluster the classification code of the classification type with a highest count of classification codes in the cluster. 14. A method according to claim 11, further comprising: displaying the clusters along one or more spines based on a similarity of the uncoded documents. 15. A method according to claim 14, further comprising: determining the spine on which the cluster is placed; and identifying a label associated with the identified spine, wherein the label is considered with the count of classification codes for each classification type and one of a presence and absence of each classification code type for the assignment of the classification code. 16. A method according to claim 14, further comprising: identifying the similar reference documents, comprising: determining a measure for the spine on which the cluster is placed; comparing the measure with each of the reference documents; and selecting the reference documents most similar to the measure as the similar reference documents. 17. A method according to claim 11, further comprising: determining a confidence level of the suggested classification; and providing the confidence level with the suggested classification. 18. A method according to claim 11, further comprising: identifying the similar reference documents, comprising: determining a measure for the one or more uncoded documents in the cluster, wherein the measure comprises one of a center of the cluster, a sample of the uncoded documents, and the cluster center and the sample; comparing the measure with each of the reference documents; and selecting the reference documents most similar to the measure as the similar reference documents. 19. A method according to claim 11, wherein the similar reference documents satisfy a threshold number. 20. A method according to claim 11, further comprising: receiving from a user, a location for injecting the similar reference documents into the cluster.