Cascaded subgroups discovery with an application to regression

Subgroup discovery is a task from the area of Knowledge Discovery in Databases (KDD) that aims at finding interesting subgroups of a population. One problem with subgroup discovery algorithms is that many of them return a very high number of subgroups, including many redundant ones. In this paper, we present an approach to iteratively build up a set of subgroups for a numerical target attribute. The result is a additive representation of the patterns in the dataset, which can also be used as a regression model. The iterative scheme presented is similar to Transformation-Based Regression (TBR), an algorithm from the area of rule-based regression. While this is work in progress, first experiments show that the resulting sets of subgroups have a predictive accuracy that is similar to that of models generated by TBR, while the models are much more compact and arguably easier to interpret.