The rapid growth of data collected and stored in various application areas brings new problems and challenges in their processing and interpretation. While database technology provides tools for data storage and “simple” querying, and statistics offers methods for analyzing small sample data, new approaches are necessary to face these challenges. These approaches are usually called knowledge discovery in databases (KDD) or data mining. KDD can be applied in various domains: banking and finance, insurance, life sciences, retail, technical diagnostics, computer networks, social networks e.t.c. Let us consider an example from medical domain, the analysis of atherosclerosis risk factors data with the aim to build a model that will differentiate between normal and risky patients.