In data mining, artificial neural networks have become of important competitors of traditional statistical melthods. They increase the potential of discovering useful knowledge in data, but only if the differences between both kinds of methods are well understood. Therefore, integrative frameworks are urgently needed. In this paper, a framework based on the calculus of observational logic is presented. Basic concepts of that framework are outlined, and it is explained how generalized quantifiers can be defined in an observational calculus to capture data mining with statistical and ANN-based methods.
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