Fuzzy data analysis with NEFCLASS

Fuzzy data analysis as we interpret it in this paper is the application of fuzzy systems to the analysis of crisp data. In this area, neuro-fuzzy systems play a very prominent role and are applied to a variety of data analysis problems like classification, function approximation or time series prediction. Fuzzy data analysis in general and neuro-fuzzy methods in particular make it easy to strike a balance between accuracy and interpretability. This is an interesting feature for intelligent data analysis and shall be discussed in this paper. 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, which can be interpreted easily. The goal is to obtain simple intuitive models for interpretation and prediction. We show how the current version of the NEFCLASS structure learning algorithms support this requirement. 2002 Elsevier Science Inc. All rights reserved.