A simple and e!ective method for selecting signi"cant input variables and determining optimal number of fuzzy rules when building a fuzzy model from data is proposed. In contrast to the existing clustering-based methods, in this approach both input selecting and partition validating are determined on the basis of a class of sub-clusters created by a self-organising network instead of on the dat...

A novel framework is described for mining fuzzy Association Rules (ARs) relating the properties of composite attributes, i.e. attributes or items that each feature a number of values derived from a common schema. To apply fuzzy Association Rule Mining (ARM) we partition the property values into fuzzy property sets. This paper describes: (i) the process of deriving the fuzzy sets (Composite Fuzz...

Abstract The fuzzy transform setting (F-transform) is proposed as a tool for representation and approximation of type-1 and type-2 fuzzy numbers; the inverse F-transform on appropriate fuzzy partition of the membership interval [0,1] is used to characterize spaces of fuzzy numbers in such a way that arithmetic operations are de…ned and expressed in terms of the F-transform of the results. A typ...

In this paper, we introduce an approach by using inverse fuzzy transforms based on the fuzzy partition with combination in collocation technique for the numerical solution of Fredholm integral equations of the second kind. The main advantage of this approach is to reduce the problem to the linear system of equations. We present the convergence theorem for this method. Finally, we give two examp...

Keywords: Perceptual grouping Early human vision Image pixel grouping Local image structure Graph partitioning Normalized cut a b s t r a c t Graph partitioning for grouping of image pixels has been explored a lot, with normalized cut based graph partitioning being one of the popular ones. In order to have a credible allegiance to the perceptual grouping taking place in early human vision, we p...

Rule-driven processing is a proven way of achieving high-speed in fuzzy processing. Up to now, ruledriven architectures where designed to work with minimum or product as T-norm. Nevertheless, a Lukasiewicz T-norm is typically used with the compositional rule of inference in expert systems applications that are based on a fuzzy inference engine. This paper presents a rule-driven processing archi...

When dealing with complex problems, it is often the case that fuzzy systems must undergo an optimization process. During this process, the preservation of interpretability is a major concern. Here we present a new mathematical framework to analyze the notion of interpretability of a fuzzy partition, and a generic algorithm to preserve it. This approach is rather flexible and it helps to highly ...

In this contribution, we propose a new method to automatically learn the Knowledge Base of a Fuzzy Rule-Based System by ®nding an appropriate Data Base using a Genetic Algorithm and considering a simple generation method to derive the Rule Base. Our genetic process learns all the components of the Data Base (number of labels, working ranges and membership function shapes for each linguistic var...

Most of the techniques used in the literature in clustering symbolic data are based on the hierarchical methodology, which utilizes the concept of agglomerative or divisive methods as the core of the algorithm. The main contribution of this paper is to show how to apply the concept of fuzziness on a data set of symbolic objects and how to use this concept in formulating the clustering problem o...

In the present paper a Korovkin-type theorem is proposed for the approximation operators defined by the inverse Ftransforms. These results allow us to choose between a variety of shapes to be used as atoms of the fuzzy partitions used within the F-transform’s framework. In this way we can enlarge considerably the class of F-transforms proposed recently by I. Perfilieva. The new fuzzy partitions...