1. A computer-implemented method for multi-category apply in a data mining system, the method comprising the steps of: receiving scoring data including a plurality of rows of data representing real-world quantities applied to a data mining model, including ranks, 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 generating 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.