A Fuzzy Mining Algorithm for Association-Rule Knowledge Discovery" (2005). ABSTRACT ABSTRASCT Due to increasing use of very large database and data warehouses, discovering useful knowledge from transactions is becoming an important research area. On the other hand, using fuzzy classification in data mining has been developed in recent years. Hong and Lee proposed a general learning method that ...

The fuzzy representation of the edges has been widely studied in different works. Generally, for each pixel, the authors use membership degrees linearly proportional to the magnitude of the gradient at that position of the image. This would be equivalent to using a triangular membership functions on the gradient magnitude. In this work we study the use of parametric functions in the transformat...

We propose two methods for tuning membership functions of a kernel fuzzy classifier based on the idea of SVM (support vector machine) training. We assume that in a kernel fuzzy classifier a fuzzy rule is defined for each class in the feature space. In the first method, we tune the slopes of the membership functions at the same time so that the margin between classes is maximized under the const...

K e y w o r d s C o m p e n s a t o r y , Fuzzy similarity measure, Inverted wedge system, Backpropagation algorithm. 1. I N T R O D U C T I O N Recently, the neural fuzzy approach to system modeling has become a popular research topic [110]. Moreover, the neural fuzzy method possesses the advantages of both the pure neural and the fuzzy methods; it brings the low-level learning and computation...

A fuzzy based method has been proposed for identification of probable capacitor nodes of radial distribution system. Simulated annealing technique has been used for final selection of the capacitor sizes. New fuzzy membership functions have been formulated where the active power membership is an exponential function of the nodal per unit active power and branch active power loss, the reactive m...

It is likely in real-world applications that only little data is available for training a knowledge-based system. We present a method for automatically training the knowledge-representing membership functions of a Fuzzy-Pattern-Classification system that works also when only little data is available and the universal set is described insufficiently. Actually, this paper presents how the Modifie...

A fuzzy approach to normalization methods for pattern verification is presented in this paper. For an input object and a claimed identity, a claimed pattern's score is calculated and compared with a given threshold to accept or reject the claimed pattern. Considering the pattern verification problem based on fuzzy set theory, the claimed pattern's score is viewed as a fuzzy membership function....

In this paper a fuzzy expert system for predicting the performance of a switched reluctance motor has been developed. The design vector consists of design parameters, and output performance variables are efficiency and torque ripple. An accurate analysis program based on Improved Magnetic Equivalent Circuit (IMEC) method has been used to generate the input-output data. These input-output data i...

The goal of data mining is to find out interesting and meaningful patterns from large databases. In some real applications, many data are quantitative and linguistic. Fuzzy data mining was thus proposed to discover fuzzy knowledge from this kind of data. In the past, two mining algorithms based on the ant colony systems were proposed to find suitable membership functions for fuzzy association r...