Novel approaches of data-mining in experimental physics

Data mining for processing experimental data in high energy and nuclear physics led to many multiparametric problems, two of them are considered: (i) hypothesis testing and classification approaches based on artificial neural networks and boosted decision trees (ii) clustering of large amounts of data by so-called growing neural gas. Some examples from the practice of the Joint Institute for Nuclear Research are given to show how to prepare data to deal with those approaches.