Experiences using decision trees for knowledge discovery

© Springer International Publishing AG 2017. Knowledge discovery is the process of identifying useful patterns from large data sets. There are two families of approaches to be used for knowledge discovery: clustering, when the classes of domain objects are not known; and inductive learning algorithms, when the classes are known and the goal is to construct a domain model useful to identify new unseen objects. Clustering algorithms have also been proposed to analyze the data when the classes are known. However, to our knowledge, inductive learning methods are not used to analyze the available data but only for prediction. What we propose here is a methodology, namely FTree, that uses a decision tree to analyze both the available data identifying patterns and some important aspects of the domain (at least from the domain’s part represented by the data at hand) such as similarity between classes, separability, characterization of classes and even some possible errors on data.