Fuzzy clustering techniques, especially fuzzy c-means (FCM) clustering algorithm, have been widely used in automated image segmentation. The performance of the FCM algorithm depends on the selection of initial cluster center and/or the initial memberships value. if a good initial cluster center that is close to the actual final cluster center can be found. the FCM algorithm will converge very q...

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...

The classification of the electrocardiogram (ECG) into different patho-physiological disease categories is a complex pattern recognition task. In this paper, we propose a scheme to integrate fuzzy c-means (FCM) clustering, principal component analysis (PCA) and neural networks (NN) for ECG beat classification. The PCA is used to decompose ECG signals into weighted sum of basic components that a...

In this paper the K-means (KM) and the Fuzzy C-means (FCM) algorithms were compared for their computing performance and clustering accuracy on different shaped cluster structures which are regularly and irregularly scattered in two dimensional space. While the accuracy of the KM with single pass was lower than those of the FCM, the KM with multiple starts showed nearly the same clustering accur...

In medical applications it is very important for a physician to be informed of patient situation as soon as possible especially in emergency circumstances. Therefore all efficient agents in patient health must be fast even medical algorithms such as clustering ones. Among clustering methods Fuzzy C-Means (FCM) clustering has been frequently used for segmentation of medical images. In this paper...

In machine learning the Fuzzy c-Means algorithm (FCM) plays an important role. This prototype based unsupervised clustering method has been extensively studied and applied to a great variety of problems from different research areas like medicine and biology. Commonly the Euclidean distance is used as dissimilarity measure, although any dissimilarity measure would be suited. Recently divergence...

Résumé. Dans cet article, nous nous intéressons à Fuzzy C-Means (FCM), une technique très connue pour la classification floue. Nous proposons un algorithme efficace basé sur la programmation DC (Difference of Convexe functions) et DCA (DC Algorithm) pour résoudre ce problème. Les expériences numériques comparatives avec l’algorithme standard FCM sur les données réelles montrent la robustesse, l...

Fuzzy C-Means (FCM) clustering is a popular technique used in image segmentation and pattern recognition. However one of the main problems with FCM clustering is the lack of spatial context. That is FCM often fails with irregularly shaped clusters. This can lead to the creation of isolated regions; isolated regions are those regions that are not connected with the main body of the clusters. We ...

In order to make up some deficiencies of the fuzzy c-means clustering algorithm, a new FCM algorithm based on pretreatment of similarity relation between samples is proposed in the paper, which is utilized to estimate the fuzzy clustering centers and the weight coefficient of samples effecting on the fuzzy clustering centers during iteration process. The new FCM algorithm makes the clustering q...