A graphical heuristic for reduction and partitioning of large datasets for scalable supervised training
Abstract A scalable graphical method is presented for selecting and partitioning datasets for the training phase of a classification task.For the heuristic, a clustering algorithm is required to get its computation cost in a reasonable proportion to the task itself.This step is succeeded by construction of an information graph of the underlying cla