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—The theory of fuzzy sets has been recognized as a suitable tool to model several kinds of patterns that can hold in data. In this paper, we are concerned with the development of a general model to discover association rules among items in a (crisp) set of fuzzy transactions. This general model can be particularized in several ways; each particular instance(More)
In this paper we introduce ALCQ + F , a fuzzy description logic with extended qualified quantification. The proposed language allows for the definition of fuzzy quantifiers of the absolute and relative kind by means of piecewise linear functions on N and Q ∩ [0, 1] respectively. These quantifiers extends the usual (qualified) ∃, ∀ and number restriction.(More)
Quanti®ed statements are used in the resolution of a great variety of problems. Several methods have been proposed to evaluate statements of types I and II. The objective of this paper is to study these methods, by comparing and generalizing them. In order to do so, we propose a set of properties that must be ful®lled by any method of evaluation of(More)
This paper presents a new family of decision list induction algorithms based on ideas from the association rule mining context. ART, which stands for 'Association Rule Tree', builds decision lists that can be viewed as degenerate, polythetic decision trees. Our method is a generalized " Separate and Conquer " algorithm suitable for Data Mining applications(More)
It has been pointed out that the usual framework to assess association rules, based on support and confidence as measures of importance and accuracy, has several drawbacks. In particular, the presence of items with very high support can lead to obtain many misleading rules, even in the order of 95% of the discovered rules in some of our experiments. In this(More)