Disclosed is an approach for performing auto-classification of documents. A machine learning framework is provided to analyze the document, where labels associated with certain documents can be propagated to other documents.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method for analyzing data, comprising: receiving data for analysis; performing feature extraction on contents of the data; performing data modelling on results from performing feature extraction; and classifying the data by analyzing similarity to perform label propagation. 2. The method of claim 1, wherein feature extraction corresponds to vectorization of the contents of the data to generate a data vector, where the data modelling is performed using the data vector and classification of the data is performed by analyzing the similarity of the data vector to other data vectors. 3. The method of claim 2, wherein the data vector comprises a term frequency vector. 4. The method of claim 1, wherein a term dictionary is updated using terms identified within the data. 5. The method of claim 1, clustering is employed to perform the data modelling. 6. The method of claim 1, wherein a similarity matrix is used to classify the data. 7. The method of claim 2, wherein metadata is obtained to vectorize the data. 8. The method of claim 1, wherein the label propagation comprises identification of neighbors for an unclassified document, and checking existing labels for the neighbors. 9. The method of claim 1, wherein a recommendation is provided as a result of the label propagation. 10. The method of claim 1, wherein a feedback process is performed such that user feedback on a label recommendation is used to enhance a subsequent iteration of the label propagation. 11. The method of claim 1, wherein the computer implemented method is performed in a cloud-based architecture. 12. A system, comprising: a processor; a memory for holding programmable code; and wherein the programmable code includes instructions for receiving data for analysis; vectorizing contents of the data to generate a data vector; performing data modelling on the data vector; and classifying the data by analyzing similarity of the data vector to other data vectors to perform label propagation. 13. The system of claim 11, wherein the data vector comprises a term frequency vector. 14. The system of claim 11, wherein a term dictionary is updated using terms identified within the data. 15. The system of claim 11, clustering is employed to perform the data modelling. 16. The system of claim 11, wherein a similarity matrix is used to classify the data. 17. The system of claim 11, wherein metadata is obtained to vectorize the data. 18. The system of claim 11, wherein the label propagation comprises identification of neighbors for an unclassified document, and checking existing labels for the neighbors. 19. The system of claim 11, wherein a recommendation is provided as a result of the label propagation. 20. The system of claim 11, wherein a feedback process is performed such that user feedback on a label recommendation is used to enhance a subsequent iteration of the label propagation. 21. The system of claim 11, wherein the system is located in a cloud-based architecture. 22. A computer program product embodied on a computer usable medium, the computer usable medium having stored thereon a sequence of instructions which, when executed by a processor causes the processor to execute a method, the method comprising: receiving data for analysis; vectorizing contents of the data to generate a data vector; performing data modelling on the data vector; and classifying the data by analyzing similarity of the data vector to other data vectors to perform label propagation. 23. The computer program product of claim 21, wherein the data vector comprises a term frequency vector. 24. The computer program product of claim 21, wherein a term dictionary is updated using terms identified within the data. 25. The computer program product of claim 21, clustering is employed to perform the data modelling. 26. The computer program product of claim 21, wherein a similarity matrix is used to classify the data. 27. The computer program product of claim 21, wherein metadata is obtained to vectorize the data. 28. The computer program product of claim 21, wherein the label propagation comprises identification of neighbors for an unclassified document, and checking existing labels for the neighbors. 29. The computer program product of claim 21, wherein a recommendation is provided as a result of the label propagation. 30. The computer program product of claim 21, wherein a feedback process is performed such that user feedback on a label recommendation is used to enhance a subsequent iteration of the label propagation. 31. The computer program product of claim 21, wherein the process is performed in a cloud-based architecture.