A parallel decision tree builder for mining very large visualization datasets

Kevin W. Bowyer, Lawrence O. Hall, Thomas Moore, Nitesh V. Chawla, W. Philip Kegelmeyer

Abstract: Simulation problems in the DOE ASCI program generate visualization datasets more than a terabyte in size. The practical difficulties in visualizing such datasets motivate the desire for automatic recognition of salient events. We have developed a parallel decision tree classifier for use in this context. Comparisons to ScalParC, a previous attempt to build a fast parallelization of a decision tree classifier, are provided. Our parallel classifier executes on the "ASCI Red" supercomputer. Experiments demonstrate that datasets too large to be processed on a single processor can be efficiently handled in parallel, and suggest that there need not be any decrease in accuracy relative to a monolithic classifier constructed on a single processor.