Nowadays fuzzy systems are frequently applied in data analysis problems like classification, function approximation or time series prediction. Here we interpret fuzzy data analysis as the application of fuzzy systems to the analysis of crisp data. The goal is to obtain simple intuitive models for interpretation and prediction. We interpret data analysis as a process that is exploratory to some extent. In order for neuro-fuzzy learning to support this aspect we require fast and simple learning algorithms that result in small rule bases. In this paper we present the current version of the NEFCLASS structure learning algorithms that support those requirements.
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