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A scalable graphical method is presented for selecting, and partitioningdatasets for the training phase of a classification task. For the heuristic, aclustering algorithm is required to get its computation cost in a reasonableproportion to the task itself. This step is proceeded by construction of aninformation graph of the underlying classification patterns using approximatenearest neighbor methods. The presented method constitutes of two approaches,one for reducing a given training set, and another for partitioning theselected/reduced set. The heuristic targets large datasets, since the primarygoal is significant reduction in training computation run-time withoutcompromising prediction accuracy. Test results show that both approachessignificantly speed-up the training task when compared against that ofstate-of-the-art shrinking heuristic available in LIBSVM. Furthermore, theapproaches closely follow or even outperform in prediction accuracy. A networkdesign is also presented for the partitioning based distributed trainingformulation. Added speed-up in training run-time is observed when compared tothat of serial implementation of the approaches.

A graphical heuristic for reduction and partitioning of large datasets for scalable supervised training