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A large hadron machine like the LHC with its high track multiplicities always asks for powerful tools that drastically reduce the large background while selecting signal events efficiently. Actually such tools are widely needed and used in all parts of particle physics. Regarding the huge amount of data that will be produced at the LHC, the process of training as well as the process of applying these tools to data, must be time efficient. Such tools can be multivariate analysis -- also called data mining -- tools. In this contribution we present the results for the application of the multivariate analysis, rule growing algorithm RIPPER on a problem of particle selection. It turns out that the meta-methods bagging and cost-sensitivity are essential for the quality of the outcome. The results are compared to other multivariate analysis techniques.

Application of the rule-growing algorithm RIPPER to particle physics analysis