Like k-means and Gaussian mixture model (GMM), fuzzy c-means (FCM) with soft partition has also become a popular clustering algorithm still is extensively studied. However, these algorithms their variants suffer from some difficulties such as determination of the optimal number clusters which key factor for quality. A common approach overcoming this difficulty to use trial-and-validation strate...

In this paper, we propose a Context-based Gustafson-Kessel (CGK) clustering that builds Information Granulation (IG) in the form of fuzzy set. The fundamental idea of this clustering is based on Conditional Fuzzy C-Means (CFCM) clustering introduced by Pedrycz. The proposed clustering develops clusters preserving homogeneity of the clustered patterns associated with the input and output space. ...

Due to its inferior characteristics, an observed (noisy) image's direct use gives rise poor segmentation results. Intuitively, using noise-free image can favorably impact segmentation. Hence, the accurate estimation of residual between and images is important task. To do so, we elaborate on residual-driven Fuzzy C-Means (FCM) for segmentation, which first approach that realizes leads participat...

Keywords: Cluster analysis Fuzzy clustering Fuzzy c-means (FCM) Initialization Bias correction Probability weight a b s t r a c t Fuzzy clustering is generally an extension of hard clustering and it is based on fuzzy membership partitions. In fuzzy clustering, the fuzzy c-means (FCM) algorithm is the most commonly used clustering method. Numerous studies have presented various generalizations o...

Recently Kernelized Fuzzy C-Means clustering technique where a kernel-induced distance function is used as a similarity measure instead of a Euclidean distance which is used in the conventional Fuzzy C-Means clustering technique, has earned popularity among research community. Like the conventional Fuzzy C-Means clustering technique this technique also suffers from inconsistency in its performa...