10. A system for multi-category apply in a data mining system comprising: a processor operable to execute computer program instructions; a memory operable to store computer program instructions executable by the processor; and computer program instructions stored in the memory and executable to perform the steps of: receiving scoring data including a plurality of rows of data representing real-world quantities applied to a data mining model including quality of fit, wherein the scoring data is received in a transactional format and is prepared to generate input data tables including active attributes and source attributes; and selecting a plurality of categories, each category representing a discrete value or a set of values of a class by generating multi-category apply data, the multi-category apply data generated by: generating input data tables including active attributes and source attributes; evaluating measures including probabilities and ranks of categories of a target attribute to determine those meeting a selection criterion for supervised models; evaluating measures including probabilities, ranks, quality of fit, and distance of predicted values to determine those meeting the selection criterion for unsupervised models; and generating an output data table including a plurality of class values of the target attribute meeting the selection criterion and their associated measures including probabilities, ranks, quality of fit, and distance based on the received scoring data.
10. A system for multi-category apply in a data mining system comprising: a processor operable to execute computer program instructions; a memory operable to store computer program instructions executable by the processor; and computer program instructions stored in the memory and executable to perform the steps of: receiving scoring data including a plurality of rows of data representing real-world quantities applied to a data mining model including quality of fit, wherein the scoring data is received in a transactional format and is prepared to generate input data tables including active attributes and source attributes; and selecting a plurality of categories, each category representing a discrete value or a set of values of a class by generating multi-category apply data, the multi-category apply data generated by: generating input data tables including active attributes and source attributes; evaluating measures including probabilities and ranks of categories of a target attribute to determine those meeting a selection criterion for supervised models; evaluating measures including probabilities, ranks, quality of fit, and distance of predicted values to determine those meeting the selection criterion for unsupervised models; and generating an output data table including a plurality of class values of the target attribute meeting the selection criterion and their associated measures including probabilities, ranks, quality of fit, and distance based on the received scoring data. 13. The system of claim 10 , wherein the step of generating multi-category apply data further comprises the steps of: receiving the scoring data in a non-transactional format; and preparing the scoring data in the non-transactional format to generate input data tables including active attributes and source attributes.