A comprehensively sized decision tree generation method for interactive data mining of very large databases

Abstract

For interactive data mining of very large databases a method working with relatively small training data that can be extracted from the target data-bases by sampling is proposed, because it takes very long time to generate decision trees for the data mining of very large databases that contain many continues data values, and size of decision trees has the tendency of dependency on the size of training data. The method proposes to use samples of confidence in proper size as the training data to generate comprehensible trees as well as to save time. For medium or small databases direct use of original data with some harsh pruning may be used, because the pruning generates trees of similar size with smaller error rates. © Springer-Verlag Berlin Heidelberg 2005.