Parallel out-of-core divide-and-conquer techniques with application to classification trees
M.K. Sreenivas, Khaled A. Alsabti, S. Ranka · 2003
Classification is an important problem in the field of data mining. Construction of good classifiers is computationally intensive and offers plenty of scope for parallelization. Divide-and-conquer paradigm can be used to efficiently construct decision tree classifiers. We discuss in detail various techniques for parallel divide-and-conquer and extend these techniques to handle efficiently disk-resident data. Furthermore, a generic technique for parallel out-of-core divide-and-conquer problems is suggested. We present pCLOUDS, the parallel version of the decision tree classifier algorithm CLOUDS, capable of handling large out-of-core data sets. pCLOUDS exhibits excellent speedup, sizeup and scaleup properties which make it a competitive tool for data mining applications. We evaluate the performance of pCLOUDS for a range of synthetic data sets on the IBM-SP2.