Partitioning algorithms and combined model integration for data mining

In this paper a data-driven procedure is introduced enabling to extract information from complex and huge data sets for statistical purposes. The proposed strategy consists of three stages: tree-partitioning, modelling and model fusion. As a result, we define a final complex decision rule for supervised classification and prediction. Main tools are represented by the tree production rules and nonlinear regression models from the class of Generalized Additive Multi-Mixture Models. The benchmark of the proposed strategy is shown using a well-known real data set.