Nowadays, decision makers in competitive and diverse business sectors must make informed decisions about strategic problems. Such problems are often complex enough to warrant the use of sophisticated analytical methods for their resolution (i.e. forecasting, classification, etc.). Having borrowed from the myriad of techniques and models available from the areas of machine learning and statistics, data mining (DM) has become a very specialized field. Consequently, the effective application of DM is littered with many difficult and technical decisions (i.e. data cleansing, feature transformations, algorithm and parameter selection, model evaluation). Most data mining products provide a large number of models and tools, but few provide "intelligent" assistance. Moreover, research seems to be based on utterly specialized DM techniques, rather than focusing on strategic, methodological, and even epistemological aspects of DM. As a result, the following work attempts to...

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