A computer implemented data driven classification and data quality checking system is provided. The system has an interface application enabled to receive data and has an associative memory software. The system has a data driven associative memory model configured to categorize one or more fields of received data and to analyze the received data. The system has a data quality rating metric associated with the received data. The system has a machine learning data quality checker for the received data, and is configured to add the received data to a pool of neighboring data, if the data quality rating metric is greater than or equal to a data quality rating metric threshold. The machine learning data quality checker is configured to generate and communicate an alert of a potential error in the received data, if the data quality rating metric is less than the data quality rating metric threshold.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented data driven classification and data quality checking system comprising: an interface application enabled to receive data; an associative memory software in communication with the interface application; a data driven associative memory model learned using the associative memory software, the data driven associative memory model configured to categorize one or more fields of the received data and configured to analyze the received data; a data quality rating metric associated with the received data and calculated by the data driven associative memory model, by comparing the received data with a pool of neighboring data in the category of field of the received data; and a machine learning data quality checker for the received data, enabled by the data driven associative memory model, and coupled to the interface application, and configured to add the received data to the pool of neighboring data, if the data quality rating metric is greater than or equal to a data quality rating metric threshold, and the machine learning data quality checker configured to generate and communicate via the interface application an alert of a potential error in the received data in the category of field of the received data, if the data quality rating metric is less than the data quality rating metric threshold. 2. The system of claim 1 further comprising reference samples of the pool of neighboring data and suggestions of a more relevant category of field of the received data, the reference samples and the suggestions provided by the data driven associative memory model, via the interface application, when the machine learning data quality checker generates and communicates via the interface application, the alert of the potential error in the received data in the category of field of the received data. 3. The system of claim 1 further comprising a computer system having one or more computers coupled to and enabling operation of the interface application and the associative memory software. 4. The system of claim 1 further comprising a star rating system having a star rating of one or more stars, a star rating accuracy level calculated for each star rating, and a star rating threshold, the data driven associative memory model normalizing the data quality rating metric to the star rating system. 5. The system of claim 1 wherein the data driven associative memory model comprises a predictive model configured to: (a) predict a model predicted classification for a classification of an instance of the received data, (b) calculate a prediction quality rating metric to include measurements of a strength and an assurance that establish a correctness of the model predicted classification, and (c) normalize the prediction quality rating metric associated with the model predicted classification to a star rating system having a star rating of one or more stars. 6. The system of claim 5 wherein the machine learning data quality checker generates and communicates via the interface application an alert of a classification potential error in the classification of the instance, if the correctness of the model predicted classification is not established. 7. The system of claim 5 wherein if the prediction quality rating metric is greater than or equal to a prediction quality rating metric threshold, then the model predicted classification is accepted and applied, and if the prediction quality rating metric is less than the prediction quality rating metric threshold, then the model predicted classification is rejected. 8. The system of claim 1 further comprising a domain vocabulary processing tool in communication with the interface application and coupled to an associative memory of the associative memory software, the domain vocabulary processing tool configured to extract relevant domain-specific terms and domain-specific phrases, and their respective variant terms and variant phrases, from a remaining free text data or a remaining source text, which have been parsed, and further configured to normalize the domain-specific terms and the domain-specific phrases, and their respective variant terms and variant phrases, into canonical designations and corresponding variants for input into an associative memory of the associative memory software. 9. The system of claim 1 further comprising a control set formed by a control set tool and a control set process, and in communication with the interface application and coupled to an associative memory of the associative memory software, the control set comprising a diverse data set and configured to classify new data sets. 10. The system of claim 9 wherein the diverse data set comprises a plurality of predefined data from a plurality of relevant categories generated by the associative memory, and a plurality of nonassociated data not associated with the plurality of relevant categories, defining a canonical phrase data set representative of canonical phrases associated with the associative memory. 11. The system of claim 1 further comprising a cluster-based batch classification tool in communication with the interface application and coupled to an associative memory of the associative memory software, the cluster-based batch classification tool configured to receive a set of matching records that are similarly grouped in a plurality of clusters, and further configured to enable batch classifying and batch evaluating of the set of matching records in the plurality of clusters simultaneously, providing a consistent classification for the set of matching records. 12. The system of claim 11 wherein the cluster-based batch classification tool is further configured to refine any records needing further search refinement, and to return similar records sorted in descending order of similarity, allowing for additional batch classifying and batch evaluating. 13. The system of claim 1 wherein the data comprises at least one of raw data, structured data, semi-structured data, unstructured data, and free text data. 14. The system of claim 1 wherein the associative memory software comprises an associative memory and a machine learning algorithm enabled to identify a plurality of associations across the received data. 15. A computer implemented data driven classification and data quality checking system comprising: an interface application enabled to receive data; an associative memory software in communication with the interface application, and incorporating an associative memory and a machine learning algorithm; a predictive model learned using the associative memory, and configured to predict a model predicted classification for a classification of an instance of received data and configured to make predictive model decisions; a prediction quality rating metric calculated by the predictive model to include measurements of a strength and an assurance that establish a correctness of the model predicted classification; wherein if the prediction quality rating metric is greater than or equal to a prediction quality rating metric threshold, then the model predicted classification is accepted and applied, and if the prediction quality rating metric is less than the prediction quality rating metric threshold, then the model predicted classification is rejected. 16. The system of claim 15 further comprising a star rating system having a star rating of one or more stars, a star rating accuracy level calculated for each star rating, and a star rating threshold, the predictive model normalizing the prediction quality rating metric to the star rating system. 17. The system of claim 16 wherein if the star rating is greater than or equal to the star rating threshold, then information derived from the predictive model is accepted, and if the star rating is less than the star rating threshold, then the information derived from the predictive model is rejected. 18. The system of claim 15 further comprising a computer system having one or more computers coupled to and enabling operation of the interface application and the associative memory software. 19. A computer implemented data driven classification and data quality checking system comprising: a computer system having one or more computers coupled to and enabling operation of an interface application coupled to an associative memory software, the interface application enabled to receive data, the associative memory software incorporating an associative memory and a machine learning algorithm enabled to identify patterns of similarities in the received data; a domain vocabulary processing tool in communication with the interface application and coupled to the associative memory of the associative memory software, the domain vocabulary processing tool configured to extract relevant domain-specific terms and domain-specific phrases, and their respective variant terms and variant phrases, from a remaining free text data or a remaining source text, which have been parsed, and further configured to normalize the domain-specific terms and the domain-specific phrases, and their respective variant terms and variant phrases, into canonical designations and corresponding variants for input into the associative memory; a control set in communication with the interface application and coupled to the associative memory of the associative memory software, the control set comprising a diverse data set and configured to classify new data sets, the diverse data set comprising a plurality of predefined data from a plurality of relevant categories generated by the associative memory, and a plurality of nonassociated data not associated with the relevant categories, defining a canonical phrase data set representative of canonical phrases associated with the associative memory; a data driven associative memory model learned using the associative memory software and configured to categorize one or more fields of the received data, the data driven associative memory model comprising a predictive model configured to predict a model predicted classification for a classification of an instance of the received data; one or more quality rating metrics including: a data quality rating metric calculated by the data driven associative memory model, as a function of comparing the received data with a pool of neighboring data in the category of field of the received data, and a prediction quality rating metric calculated by the predictive model to include measurements of a strength and an assurance that establish a correctness of the model predicted classification; a machine learning data quality checker for the received data, enabled by the data driven associative memory model and coupled to the interface application, and configured to: accept and add the received data to the pool of neighboring data, if the data quality rating metric is greater than or equal to a data quality rating metric threshold; and accept and apply the model predicted classification, if the correctness of the model predicted classification is established, and if the prediction quality rating metric is greater than or equal to a prediction quality rating metric threshold; wherein the machine learning data quality checker is further configured to: generate and communicate via the interface application an alert of a potential error in the received data in the category of field of the received data, if, the data quality rating metric is less than the data quality rating metric threshold; and generate and communicate via the interface application an alert of a classification potential error in the classification of the instance of the received data, if the correctness of the model predicted classification is not established, or if the prediction quality rating metric is less than the prediction quality rating metric threshold. 20. The system of claim 19 further comprising a star rating system comprising star ratings of one or more stars, the data driven associative memory model normalizing the one or more quality rating metrics to the star rating system, the star rating system further comprising a star rating accuracy level calculated for each star rating, and a star rating threshold based on an accuracy level required by a process. 21. The system of claim 19 further comprising a cluster-based batch classification tool in communication with the interface application and coupled to an associative memory of the associative memory software, the cluster-based batch classification tool configured to receive a set of matching records that are similarly grouped in a plurality of clusters, and further configured to enable batch classifying and batch evaluating of the set of matching records in the plurality of clusters simultaneously, providing a consistent classification for the set of matching records.