[1] In this paper, classification of a large hydrochemical data set (more than 600 water samples and 11 hydrochemical variables) from southeastern California by fuzzy c-means (FCM) and hierarchical cluster analysis (HCA) clustering techniques is performed and its application to hydrochemical facies delineation is discussed. Results from both FCM and HCA clustering produced cluster centers (prot...

In this paper, an improved fuzzy c-means (IFCM) clustering algorithm for image segmentation is presented. The originality of this algorithm is based on the fact that the conventional FCM-based algorithm considers no spatial context information, which makes it sensitive to noise. The new algorithm is formulated by incorporating the spatial neighborhood information into the original FCM algorithm...

Fuzzy c-means (FCM) is an important clustering algorithm. However, it does not consider the impact of different feature on clustering. In this paper, we present a fuzzy clustering algorithm with the generalized entropy of feature weights FCM (GEWFCM). By introducing feature weights and adding regularized term of their generalized entropy, a new objective function is proposed in terms of objecti...

Despite its potential advantages for fMRI analysis, fuzzy C-means (FCM) clustering suffers from limitations such as the need for a priori knowledge of the number of clusters, and unknown statistical significance and instability of the results. We propose a randomizationbased method to control the false positive rate and estimate statistical significance of the FCM results. Using this novel appr...

In this paper, a fast and practical GPU-based implementation of Fuzzy C-Means (FCM) clustering algorithm for image segmentation is proposed. First, an extensive analysis is conducted to study the dependency among the image pixels in the algorithm for parallelization. The proposed GPU-based FCM has been tested on digital brain simulated dataset to segment white matter(WM), gray matter(GM) and ce...

Many variants of fuzzy c-means (FCM) clustering method are applied to crisp numbers but only a few of them are extended to non-crisp numbers, mainly due to the fact that the latter needs complicated equations and exhausting calculations. Vector form of fuzzy c-means (VFCM), proposed in this paper, simplifies the FCM clustering method applying to non-crisp (symbolic interval and fuzzy) numbers. ...

BACKGROUND The optimal iron therapy regimen in patients with non-dialysis-dependent chronic kidney disease (CKD) is unknown. METHODS Ferinject® assessment in patients with Iron deficiency anaemia and Non-Dialysis-dependent Chronic Kidney Disease (FIND-CKD) was a 56-week, open-label, multicentre, prospective and randomized study of 626 patients with non-dialysis-dependent CKD, anaemia and iron...

Linear infrastructures represent one of the most important human impacts on natural habitats and exert several effects on mammal populations. Motorways are recognized as a major cause of habitat fragmentation and degradation and of biodiversity loss. However, it is unknown whether motorways lead to increased physiological stress reactions in wild animal populations. We analysed faecal corticost...

the idea of this paper is designing an automatic fault detection system based on fuzzy logic, therefore two signals of pmsm in fault condition are analyzed for inter turn fault detection: current and torque. in this fault type there is some distortion in these signals, but it is not good enough to detecting with fuzzy logic solely, so with combination of wavelet transform and fcm a new method f...