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 ...

This paper presents an investigation into two fuzzy association rule mining models for enhancing prediction performance. The first model (the FCM-Apriori model) integrates Fuzzy C-Means (FCM) and the Apriori approach for road traffic performance prediction. FCM is used to define the membership functions of fuzzy sets and the Apriori approach is employed to identify the Fuzzy Association Rules (...

This paper presents a latest survey of different technologies used in medical image segmentation using Fuzzy C Means (FCM).The conventional fuzzy c-means algorithm is an efficient clustering algorithm that is used in medical image segmentation. To update the study of image segmentation the survey has performed. The techniques used for this survey are Brain Tumor Detection Using Segmentation Bas...

Medical image segmentation plays a vital role in image processing due to the catering needs of the medical images in automating, delineating anatomical structures and diagnosis. Very often the medical images contain uncertain, vague, and incomplete data definition. The concepts of lower and upper approximations of rough sets effectively handle this data. In this paper, rough sets based clusteri...

Gath–Geva (GG) algorithm is one of the most popular methodologies for fuzzy c-means (FCM)-type clustering of data comprising numeric attributes; it is based on the assumption of data deriving from clusters of Gaussian form, a much more flexible construction compared to the spherical clusters assumption of the original FCM. In this paper, we introduce an extension of the GG algorithm to allow fo...

Fuzzy C-Means Clustering algorithm (FCM) is a method that is frequently used in pattern recognition. It has the advantage of giving good modeling results in many cases, This paper presents the optimization of cluster center of Fuzzy C-Means algorithm by evolutionary methods, this in order to automatically select the best of cluster center with maximum probability. Optimization methods used to r...

This paper introduces the fuzzy scatter partition-based fuzzy inference system to construct the model for nonlinear process to analyze nonlinear characteristics. The fuzzy rules of fuzzy inference systems are generated by partitioning the input space in the scatter form using Fuzzy C-Means (FCM) clustering algorithm. The premise parameters of the rules are determined by membership matrix by mea...

Image Classification is the evolution of separating or grouping an image into different parts. The good act of recognition algorithms based on the quality of classified image. The good feat of recognition algorithms based on the quality of classified image. An important problem in SAR image application is accurate classification. Image segmentation is the mainly practical loom among virtually a...

As one of the most common data mining techniques, clustering has been widely applied in many fields, among which fuzzy clustering can reflect the real world in a more objective perspective. As one of the most popular fuzzy clustering algorithms, Fuzzy C-Means (FCM) clustering combines the fuzzy theory and K-Means clustering algorithm. However, there are some issues with FCM clustering. For exam...