Skip to Main Content
Decision tree (DT) induction is regarded as a representative of traditional approaches to classification rule mining which is an important technique for many data mining applications. Using a heuristic-based local search, DT induction appends attribute at a time to rules in the order of goodness. This method may eliminate some typical structures that several attributes collectively determine the class. Recently, there has been growing interest in the problem of discovering functional dependencies (FDs) from existing databases [[Flach et al.], [Y. Huhtala et al., (1999)], [Lopes et al.], [Novelli et al.]]. Some efficient and scalable algorithms have been proposed. In this paper, we present a new method to build a DT classifier using approximate FDs [Y. Huhtala et al., (1999)]. The new method is different from the traditional ways of building DTs in that it searches composite attributes for individual node of a DT which leads to substantially smaller and more understandable DTs without adversely affecting the accuracy gains. Experiments showed that the new method not only builds more accurate classifiers, but also does this with more compact structures.