The Parallel Classification of Very Large Collections of Data on Multi-core Platforms

Perhaps the most utilized and demanded task in data mining is classification. Most existing classification algorithms require all the data used for constructing the model for classification, or at least a good part of it, to be stored in the memory. This makes them limited by the availability of the memory. We present a parallel algorithm, based on the SPRINT decision tree, which eliminates the dependency on the memory available by storing the data to be processed in a database.