Automatic generation of fuzzy rules in ill-structured domains with numerical variables

In this report, an application of a methodology of automatic generation of conceptual descriptions for characterizing a given partition in an ill-structured domain is presented. A specific application on a wastewater treatment process (wwtp) illustrates the behaviour of this methodology. The methodology is based on the combination of statistical tools and inductive learning, in such a way that the nature of the data is preserved, avoiding previous transformations of the variables. Thus qualitative and quantitative information can be induced from data. This information is useful for the automatic generation of a system of fuzzy rules, which, in turn, allows the posterior recognition of the obtained classes. In previous works it has been proved that the multiple box-plot is a useful and powerful statistical tool for distinguishing classes by means of numerical variables. It constitutes the basis for the methodology presented here, which permits detection of relevant variables characterized of any classes. In this report, we propose the first version of a formal methodology having as an objective the automatic generation of conceptual class descriptions. The goal is to characterize the various situations that can arise in a day at a wastewater treatment plant (relevant information to facilitate the plant's managing the decision making processes).Postprint (published version