Fuzzy Cognitive Maps (FCMs) have successfully been applied in numerous domains to show relations between essential components. In some FCM, there are more nodes, which related to each other and more nodes means more complex in system behaviors and analysis. In this paper, a novel learning method used to construct FCMs based on historical data and by using data mining and DEMATEL method, a new m...

Clustering algorithm is very important for data mining. Fuzzy c-means clustering algorithm is one of the earliest goal-function clustering algorithms, which has achieved much attention. This paper analyzes the lack of fuzzy C-means (FCM) algorithm and genetic clustering algorithm. Propose a hybrid clustering algorithm based on immune single genetic and fuzzy C-means. This algorithm uses the fuz...

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, although, it is not capable of specifying the number of clusters by itself. Aimed at the problems existed in the FCM clustering algorithm, a kernelbased fuzzy c-means (KFCM) is clustering algorithm is proposed to optimize fuzzy ...

Image segmentation plays a significant role in computer vision. It aims at extracting meaningful objects lying in the image. Generally there is no unique method or approach for image segmentation. Clustering is a powerful technique that has been reached in image segmentation. The cluster analysis is to partition an image data set into a number of disjoint groups or clusters. The clustering meth...

The traditional fuzzy C-means(FCM) algorithm is an optimization algorithm based on gradient descending, it is sensitive to the initial condition and liable to be trapped in a local optimum. Search space smoothing allows a local search heuristics to escape from a poor, local optimum. In this paper, an improved FCM algorithm based on search space smoothing is proposed. By designing a proper smoot...

Accurate medical diagnosis requires a segmentation of large number of medical images. The automatic segmentation is still challenging because of low image contrast and ill-defined boundaries. Image segmentation refers to the process that partitions an image into mutually exclusive regions that cover the image. Among the various image segmentation techniques, traditional image segmentation metho...

The exceptional growth of graphics hardware in programmability and data processing speed in the past few years has fuelled extensive research in using it for general purpose computations more than just image-processing and gaming applications. We explore the use of graphics processors (GPU) to speedup the computations involved in Fuzzy c-means (FCM). FCM is an important iterative clustering alg...

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...

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, although, it is not capable of specifying the number of clusters by itself. In FCM algorithm most researchers fix weighting exponent (m) to a conventional value of 2 which might not be the appropriate for all applications. Conse...

Some of the well-known fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. Gustafson-Kessel clustering algorithm and Gath-Geva clustering algorithm were developed to detect non-spherical structural clusters. However, the former needs added constraint of fuzzy covariance matrix, the later can only be used for the d...