This paper presents a fuzzy clustering algorithm, called the alternative fuzzy c-numbers (AFCN) clustering algorithm, for LR-type fuzzy numbers based on an exponential-type distance function. On the basis of the gross error sensitivity and in7uence function, this exponential-type distance is claimed to be robust with respect to noise and outliers. Hence, the AFCN clustering algorithm is more ro...

Semi-supervised clustering is an important method which can improve clustering performance by introducing partial supervised information. This paper mainly studies the semi-supervised fuzzy clustering based on Mahalanobis distance and Gaussian Kernel for SCAPC algorithm. Here, we give a new semi-supervised fuzzy clustering objective function. By solving the optimization problem with above objec...

Suppressed fuzzy c-means (S-FCM) clustering algorithm with the intention of combining the higher speed of hard c-means clustering algorithm and the better classification performance of fuzzy c-means clustering algorithm had been studied by many researchers and applied in many fields. In the algorithm, how to select the suppressed rate is a key step. In this paper, we give a method to select the...

Fuzzy clustering methods are conveniently employed in constructing a fuzzy model of a system, but they need to tune some parameters. In this research, FCM is chosen for fuzzy clustering. Parameters such as the number of clusters and the value of fuzzifier significantly influence the extent of generalization of the fuzzy model. These two parameters require tuning to reduce the overfitting in the...

The present work introduce a novel fuzzy approach to deal with the problem of parameter selection in the Evolutionay Computation EC Algorithms. In our approach a fuzzy clustering algorithm is used instead of a rule based system.The Fuzzy clustering gives freedom to the genetic algorithm to evolve simultaneously with the EC population.The crossover and mutation rates are optimized dynamically in...

background: in this paper we compare a highly accurate supervised to an unsupervised technique that uses breast thermal images with the aim of assisting physicians in early detection of breast cancer. methods: first, we segmented the images and determined the region of interest. then, 23 features that included statistical, morphological, frequency domain, histogram and gray-level co-occurrence ...

Fuzzy systems are capable to model the inherent uncertainties in real world problems and implement human decision making. In this paper two issues related to fuzzy systems development are addressed and solutions are proposed and implemented. First issue is related to the high dimensional data sets. Such kinds of data sets lead to explode the search space of generated rules and results into dete...

Clustering is a standard approach in analysis of data and construction of separated similar groups. The most widely used robust soft clustering methods are fuzzy, rough and rough fuzzy clustering. The prominent feature of soft clustering leads to combine the rough and fuzzy sets. The Rough Fuzzy C-Means (RFCM) includes the lower and boundary estimation of rough sets, and fuzzy membership of fuz...