Inductive learning algorithms try to obtain the knowledge of a system from a set of examples. One of the most diicult problems in machine learning consists in getting the structure of this knowledge. We propose an algorithm able to manage with fuzzy information and able to learn the structure of the rules that represent the system. The algorithm gives a reasonable small set of fuzzy rules that ...

The learning of a Fuzzy Rule-Based Classification System (FRBCS) by means of a supervised inductive process fundamentally implies four tasks that are complementary among them: the selection of the most informative variables to the classification problem to solve, the generation of a set of rules, the selection of the subset of rules with the best co-operation and the least redundancy, and the e...

Mining hybrid dimension fuzzy association rule is one of the important processes in data mining . Apriori algorithm concerned with handling single level, single dimensional association rules. this paper is presenting, a new modification in joining process to reduce the redundant generation of sub items during pruning the candidate itemsets, which can obtain higher efficiency of mining that of o...

Causal inference is one of the central capabilities of the natural intelligence that plays a crucial role in thinking, perception, and problem solving. Fuzzy inferences are an extended form of formal inferences that provide a denotational mathematical means for rigorously dealing with degrees of matters, uncertainties, and vague semantics of linguistic variables, as well as for rational reasoni...

In this paper, we firstly invoke gradual Hausdorff metric to define a new additive fuzzy-valued measure on the ordinary measurable space. Then, from the view of a fuzzy number as a crisp interval of gradual numbers, we show that the new fuzzy-valued measure can be characterized by two gradual number-valued measures. Finally, we investigate some of its properties and structural characterizations.

In fuzzy clustering algorithms each object has a fuzzy membership associated with each cluster indicating the degree of association of the object to the cluster. Here we present a fuzzy subspace clustering algorithm, FSC, in which each dimension has a weight associated with each cluster indicating the degree of importance of the dimension to the cluster. Using fuzzy techniques for subspace clus...

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...

For intuitionistic fuzzy sets, it is argued that three dimensional distance functions are not necessary because two dimensional distance functions already provide a simple and concise expression of the distance between two intuitionistic fuzzy sets. However, we show that a three dimensional interpretation of intuitionistic fuzzy sets could give different comparison results to the ones obtained ...

In this study, we explored hybrid fuzzy logic modelling techniques to predict the burned area of forest fires. Fast detection is crucial for successful firefighting, and a model with an accurate prediction ability extremely useful optimizing fire management. Fuzzy Inductive Reasoning (FIR) Adaptive Neuro-Fuzzy Inference System (ANFIS) are two powerful areas forests in Portugal. The results obta...