The fuzzy time series approaches, which recently are intensively considered by the researchers, consist of three stages of fuzzification, determination of fuzzy relations and defuzzification. Several studies using different approaches in these steps have been conducted in literature. In most of the studies related fuzzy time series, the membership degrees of belonging to every fuzzy set of each...

using greedy clustering method to solve capacitated location-routing problem with fuzzy demands abstract in this paper, the capacitated location routing problem with fuzzy demands (clrp_fd) is considered. in clrp_fd, facility location problem (flp) and vehicle routing problem (vrp) are observed simultaneously. indeed the vehicles and the depots have a predefined capacity to serve the customerst...

this paper presents a fuzzy decision-making approach to deal with a clustering supplier problem in a supply chain system. during recent years, determining suitable suppliers in the supply chain has become a key strategic consideration. however, the nature of these decisions is usually complex and unstructured. in general, many quantitative and qualitative factors, such as quality, price, and fl...

The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal fuzzy data. Then, as an application, a method based on the proposed algorithm is pres...

breast cancer is the second largest cause of cancer deaths among women. at the same time, it is also among the most curable cancer types if it can be diagnosed early. this paper presents a novel hybrid intelligent method for recognition of breast cancer tumors. the proposed method includes three main modules: the feature extraction module, the classifier module and the optimization module. in t...

this paper presents an efficient hybrid method, namely fuzzy particleswarm optimization (fpso) and fuzzy c-means (fcm) algorithms, to solve the fuzzyclustering problem, especially for large sizes. when the problem becomes large, thefcm algorithm may result in uneven distribution of data, making it difficult to findan optimal solution in reasonable amount of time. the pso algorithm does find ago...

 In this paper, utilization of clustering algorithms for data fusion in decision level is proposed. The results of automatic isolated word recognition, which are derived from speech spectrograph and Linear Predictive Coding (LPC) analysis, are combined with each other by using fuzzy clustering algorithms, especially fuzzy k-means and fuzzy vector quantization. Experimental results show that the...

Abstract-This paper presents a general approach to fuzzy clustering methods. A generalised fuzzy objective function is used to combine fuzzy c-means clustering, fuzzy entropy clustering, and their extended versions into a generalised fuzzy clustering method. Some new extended versions of the above-mentioned clustering methods are proposed from this general approach. Several cluster data sets we...

This paper describes two classic style methods to analyze and segment the color space. The RGB space method includes color space pyramiding, low-pass filtering, 3-D object labeling and property calculation to acquire a proper number of colors and a good initial estimate of center positions, then fuzzy cmeans algorithm can be used to optimally cluster the color space distribution points. The sec...

This paper presents a novel image processing procedure dedicated to the automated detection of the medial axis of the root canal from dental micro CT records. The 3D model of root canal is built up from several hundreds of parallel cross sections, using image enhancement and an enhanced fuzzy cmeans based partitioning, center point detection in the segmented slice, three dimensional inner surfa...