Clustering is an important research topic that has practical applications in many 5elds. It has been demonstrated that fuzzy clustering, using algorithms such as the fuzzy C-means (FCM), has clear advantages over crisp and probabilistic clustering methods. Like most clustering algorithms, however, FCM and its derivatives need the number of clusters in the given data set as one of their initiali...

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...

A hybrid neural network regression models with unsupervised fuzzy clustering is proposed for clustering nonparametric regression models for datasets. In the new formulation, (i) the performance function of the neural network regression models is modified such that the fuzzy clustering weightings can be introduced in these network models; (ii) the errors of these network models are feed-backed i...

White matter lesions are small areas of dead cells found in parts of the brain that act as connectors are detected using magnetic resonance imaging (MRI) which has increasingly been an active and challenging research area in computational neuroscience. This paper presents new image segmentation models for automated detection of white matter changes of the brain in an elderly population. The mai...

Fuzzy C-Mean (FCM) is an unsupervised clustering algorithm based on fuzzy set theory that allows an element to belong to more than one cluster. Where fuzzy means “unclear” or “not defined” and c denotes “clustering”. In FCM the number of cluster are randomly selected. [15] FCM is the advanced version of K-means clustering algorithm and doing more work than K-means. K-Means just needs to do a di...

Clustering is regarded as one of the significant task in data mining and has been widely used in very large data sets. Soft clustering is unlike the traditional hard clustering which allows one data belong to two or more clusters. Soft clustering such as fuzzy c-means and rough k-means have been proposed and successfully applied to deal with uncertainty and vagueness. However, the influx of ver...

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...

Clustering is the problem of grouping objects on the basis of a similarity measure between them. This paper considers the approaches belonging to the K-means family, in particular those based on fuzzy memberships. When patterns are represented by means of non-metric pairwise dissimilarities, these methods cannot be directly applied, since they are not guaranteed to converge. Symmetrization and ...

Middle spatial resolution multi-spectral remote sensing image is a kind of color image with low contrast, fuzzy boundaries and informative features. In view of these features, the fuzzy C-means clustering algorithm is an ideal choice for image segmentation. However, fuzzy C-means clustering algorithm requires a pre-specified number of clusters and costs large computation time, which is easy to ...

In this paper, we apply K-means and Fuzzy C-Means, two widely used clustering algorithms, to cluster a lymph node tissue section which had been diagnosed with metastatic infiltration (cancer spread from its original location). Each cluster algorithm is run 10 times as different initialisation states may lead to different clustering results. We compare the performance of the two algorithms by su...