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Principal Component Analysis (PCA) is a well-known tool often used for the exploratory analysis of a numerical data set. Here an extension of classical PCA is proposed, which deals with fuzzy data (in short PCAF), where the elementary datum cannot be recognized exactly by a speciÿc number but by a center, two spread measures and a membership function.(More)
The problem of regression analysis in a fuzzy setting is discussed. A general linear regression model for studying the dependence of a LR fuzzy response variable on a set of crisp explanatory variables, along with a suitable iterative least squares estimation procedure, is introduced. This model is then framed within a wider strategy of analysis, capable to(More)
In possibilistic clustering the objects are assigned to clusters according to the so-called membership degrees taking values in the unit interval. Differently from fuzzy clustering, it is not required that the sum of the membership degrees of an object in all the clusters is equal to one. This is very helpful in the presence of outliers, which are usually(More)
This paper describes the Multimedia Educational Pills (MEPs) model. MEPs are highly concentrated courses, designed to address a topic through multiple representations, following a recursive, non-cumulative, logic, as pointed out by the Cognitive Flexibility Theory (CFT) methodology. MEPs have been designed to meet the educational challenges posed by the(More)