The construction of interpretable Takagi--Sugeno (TS) fuzzy models by means of clustering is addressed. First, it is shown how the antecedent fuzzy sets and the corresponding consequent parameters of the TS model can be derived from clusters obtained by the Gath--Geva algorithm. To preserve the partitioning of the antecedent space, linearly transformed input variables can be used in the model. This may, however, complicate the interpretation of the rules. To form an easily interpretable model that does not use the transformed input variables, a new clustering algorithm is proposed, based on the Expectation Maximization (EM) identification of Gaussian mixture models. This new technique is applied to two well-known benchmark problems: the MPG (miles per gallon) prediction and a simulated second-order nonlinear process. The obtained results are compared with results from the literature.

The classical fuzzy classifier consists of rules each one describing one of the classes. In this paper a new fuzzy model structure is proposed where each rule can represent more than one classes with different probabilities. The obtained classifier can be considered as an extension of the quadratic Bayes classifier that utilizes mixture of models for estimating the class conditional densities. A supervised clustering algorithm has been worked out for the identification of this fuzzy model. The relevant input variables of the fuzzy classifier have been selected based on the analysis of the clusters by FisherÕs interclass separability criteria. This new approach is applied to the well-known wine and Wisconsin breast cancer classification problems.

www.fmt.vein.hu/softcomp Abstract. Fuzzy decision tree induction algorithms require the fuzzy quantization of the input variables. This paper demonstrates that supervised fuzzy clustering combined with similarity-based rule-simplification algorithms is an effective tool to obtain the fuzzy quantization of the input variables, so the synergistic combination of supervised fuzzy clustering and fuzzy decision tree induction can be effectively used to build compact and accurate fuzzy classifiers.

The qualitative identification to determine the origin (i.e. manufacturing factory) of Portuguese clinkers is described. The classification of clinkers produced in different factories can be based on their trace element content. Approximately 24 clinker sorts are analysed, collected from all Portuguese cement factories to determine their Mg, Sr, Ba, Mn, Ti, Zr, Zn and V content. An expert system can be formulated by crisp or fuzzy decision tree is designed based on the collected data. The performance of the obtained classifiers was measured by ten-fold cross validation. The results show that the proposed methods are useful to identify easy-to-use expert systems that are able to determine the origin of the clinker based on its trace element content.

Abstract not found