The problem of mining association rules for fuzzy quantitative items was introduced and an algorithm proposed in [7]. However, the algorithm assumes that fuzzy sets are given. In this paper we propose a method to nd the fuzzy sets for each quantitative attribute in a database by using clustering techniques. We present a scheme for nding the optimal partitioning of a data set during the clusteri...

This paper presents a switch methodology together with a sum of squares (SOS) techniques to synthesize a nonlinear controller for a polynomial Takagi-Sugeno (TS) fuzzy model. A polynomial T-S fuzzy model adopts a polynomial representation of the nonlinear dynamics in its consequent part, which make it less susceptible to linearization errors. With respect to polynomial T-S fuzzy models, a fuzzy...

Causal networks (CNs) have been used to construct inference systems for diagnostics and decision making. More recently, Bayesian causal networks (BCNs) and fuzzy causal networks (FCNs) have gained considerable attention and offer an alternative framework for representing structured human knowledge and are used in causal inference in many real-world applications. However, for large systems, it i...

The main purpose of this paper is to prove the existence of the fuzzy core of an exchange economy with a heterogeneous divisible commodity in which preferences of players are concave measures defined on a σ-algebra of admissible pieces of the total endowment of the commodity. The problem is formulated as the partitioning of a measurable space among finitely many players. Applying the Yosida– He...

Fuzzy Hamming Distance is successfully used in a Content-Based Image Retrieval (CBIR) system as a similarity measure. The system performs a m × n partitioning of the compared images and for each partitions pairs evaluates FHD. In the last step the FHD are defuzzified and the results are combined in a final score. In order to take full advantage of the use of fuzzy sets, the current study invest...

In this paper a new cluster validity index is introduced, which assesses the average compactness and separation of fuzzy partitions generated by the fuzzy c-means algorithm. To compare the performance of this new index with a number of known validation indices, the fuzzy partitioning of two data sets was carried out. Our validation performed favorably in all studies, even in those where other v...

Image segmentation, which is an important stage of many image processing algorithms, is the process of partitioning an image into nonintersecting regions, such that each region is homogeneous and the union of no two adjacent regions is homogeneous. This paper presents a novel pixon-based algorithm for image segmentation. The key idea is to create a pixon model by combining fuzzy filtering as a ...

We propose an image segmentation algorithm based on local measures and fuzzy feature space analysis. We address the problem of assigning individual pixels to multiple classes based on computed pixel properties. This new segmentation algorithm extends an object/background segmentation approach introduced in 1992. The idea of analyzing trajectories in fuzzy feature space leads to the partitioning...

The design of neuro-fuzzy networks based on fuzzy respective input space for pattern recognition is introduced in this paper. The premise part of the rules of the proposed networks is realized by partitioning of the fuzzy respective input space. The respectively partitioned spaces express the rules of the networks. The consequence part of the rules is represented by polynomial functions. The co...

In this paper, a novel modified evolutionary fuzzy clustering has been proposed. This technique exploits parameters such as Minkowski distance, Xie Beni index, and classification entropy. These parameters turn to account to polish the performance measure by incorporating shape of the cluster, class compactness and partitioning quality respectively in standard Fuzzy C-Means clustering. Moreover,...