  In this paper a data mining approach for variable selection and knowledge
extraction from datasets is presented. The approach is based on unguided
symbolic regression (every variable present in the dataset is treated as the
target variable in multiple regression runs) and a novel variable relevance
metric for genetic programming. The relevance of each input variable is
calculated and a model approximating the target variable is created. The
genetic programming configurations with different target variables are executed
multiple times to reduce stochastic effects and the aggregated results are
displayed as a variable interaction network. This interaction network
highlights important system components and implicit relations between the
variables. The whole approach is tested on a blast furnace dataset, because of
the complexity of the blast furnace and the many interrelations between the
variables. Finally the achieved results are discussed with respect to existing
knowledge about the blast furnace process.
