Fuzzy clustering is a widely used approach for data classification by using the fuzzy set theory. The probability measure and the possibility measure are two popular measures which have been used in the fuzzy c-means algorithm (FCM) and the possibilistic clustering algorithms (PCAs), respectively. However, the numerical experiments revealed that FCM and its derivatives lack the intuitive concep...

As one of the most popular and well-recognized clustering methods, fuzzy C-means (FCM) clustering algorithm is the basis of other fuzzy clustering analysis methods in theory and application respects. However, FCM algorithm is essentially a local search optimization algorithm. Therefore, sometimes, it may fail to find the global optimum. For the purpose of getting over the disadvantages of FCM a...

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 an optimized fuzzy logic based segmentation for abnormal MRI brain images analysis is presented. A conventional fuzzy c-means (FCM) technique does not use the spatial information in the image. In this research, we use a FCM algorithm that incorporates spatial information into the membership function for clustering. The FCM algorithm that incorporates spatial information into the m...

We present a swarm intelligence approach to data clustering. Data is clustered without initial knowledge of the number of clusters. Ant based clustering is used to initially create raw clusters and then these clusters are refined using the Fuzzy C Means algorithm. Initially the ants move the individual objects to form heaps. The centroids of these heaps are taken as the initial cluster centers ...

A fuzzy c-means (FCM) variant is proposed for the generation of fuzzy term sets with 2 overlap. The proposed variant di5ers from the original mainly in two areas. The 6rst modi6cation ensures that two end terms take the maximum and minimum domain values as their centers. The second modi6cation prevents the generation of non-convex fuzzy terms that often occurs with the original algorithm. The o...

Image segmentation is one of the most important tasks to extract information in image processing. To satisfy increasing requirement of image segmentation, a variety of segmentation methods have been developed over the past several years. Fuzzy c-means (FCM) is unsupervised segmentation technique that has been successfully applied to future analysis, clustering, and classification but the FCM an...