Abstract: A computerized method, system and program product for generating a data mining model. A user can provide objectives for the model and sample data to train, validate, and test the model. A rules system can automatically select a set of algorithms based on the objectives and/or sample data. A plurality of datasets can also be created from the sample data. Using the datasets, the set of algorithms can be optimized for the particular data on which it is intended to be used. The data mining model can then be generated from the optimized set of algorithms.

Abstract: A computerized method, system and program product for generating a data mining model. A user can provide objectives for the model and sample data to train, validate, and test the model. A rules system can automatically select a set of algorithms based on the objectives and/or sample data. A plurality of datasets can also be created from the sample data. Using the datasets, the set of algorithms can be optimized for the particular data on which it is intended to be used. The data mining model can then be generated from the optimized set of algorithms.

Abstract: A computerized method, system and program product for generating a data mining model. A user can provide objectives for the model and sample data to train, validate, and test the model. A rules system can automatically select a set of algorithms based on the objectives and/or sample data. A plurality of datasets can also be created from the sample data. Using the datasets, the set of algorithms can be optimized for the particular data on which it is intended to be used. The data mining model can then be generated from the optimized set of algorithms.

Abstract: A computerized method, system and program product for generating a data mining model. A user can provide objectives for the model and sample data to train, validate, and test the model. A rules system can automatically select a set of algorithms based on the objectives and/or sample data. A plurality of datasets can also be created from the sample data. Using the datasets, the set of algorithms can be optimized for the particular data on which it is intended to be used. The data mining model can then be generated from the optimized set of algorithms.

Abstract: An improved solution for evaluating one or more data mining algorithms. A set of goals for the data mining algorithm(s) is obtained, and a weight can be assigned to each goal. Each data mining algorithm is applied to a dataset to generate a set of results. A performance value for each data mining algorithm can be calculated based on the weights and set of results. When multiple data mining algorithms are being evaluated, their respective performances can be compared using the respective sets of results and performance values.

Abstract: Under the present invention, a data exploration system, a customized model system and an existing model system are provided. The data exploration system analyzes user data to identify statistical information such as data distribution, data relationships, data outliners and invalid or missing data values. The customized model center iteratively generates customized data mining models in parallel based on permutations of the user data, user-provided business parameters and/or a set of model generation algorithms. The existing model system provides users with a library of existing data mining models, assembled based on the business parameters, from which they can choose one or more. In any event, any customized or existing data mining models selected can be run against the user data in parallel.

Abstract: Under the present invention, columns of the user data schema are first matched to corresponding columns of the mining model schema. Once the columns are matched, it will be determined whether data within matching columns of the user data schema has a data type different than data within the corresponding columns of the mining model schema. If so, the data within the matching columns of the user data schema is transformed to match the data type of the data within the corresponding columns of the mining model schema. After any transformation is performed, the user/operator is provided with an opportunity to alter or override the mapping. Once the final mapping is provided, one or more matching resources can be updated to reflect the mapping.