In this paper we introduce Median Fuzzy C-Means (MFCM). This algorithm extends the Median C-Means (MCM) algorithm by allowing fuzzy values for the cluster assignments. To evaluate the performance of M-FCM, we compare the results with the clustering obtained by employing MCM and Median Neural Gas (MNG).

In this paper, the performance of the various fuzzy based algorithms for medical image segmentation is presented. Fuzzy c-means (FCM) algorithm has proved its effectiveness for image segmentation. However, still it lacks in getting robustness to noise and outliers, especially in the absence of prior knowledge of the noise. To overcome this problem, different types of fuzzy algorithms are introd...

This paper presents an approach to medical image registration using a segmentation step segmentation based on Fuzzy C-Means (FCM) clustering and the Scale Invariant Feature Transform (SIFT) for matching keypoints in segmented regions. To obtain robust segmentation, FCM is applied on feature vectors composed by local information invariant to image scaling and rotation, and to change in illuminat...

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 randomization-based method to control the false-positive rate and estimate statistical significance of the FCM results. Using this novel app...

 Clustering is a challenging problem in data mining, requiring both accurate determination of the number of clusters and correct clustering of the data. Fuzzy C-means (FCM) is a popular algorithm using the partitioning approach to solve this problem. A drawback to FCM is that it requires the number of clusters to be set a priori. In this study, we combine FCM with Genetic Algorithm (GA), Subtr...

Data clustering is a key task for various processes including sequence analysis and pattern recognition. This paper studies a clustering algorithm that aimed to increase accuracy and sensitivity when working with biological data such as DNA sequences. The new algorithm is a modified version of fuzzy C-means (FCM) and is based on the well-known self-organizing map (SOM). In order to show the per...

Breast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early. The automatic diagnosis of breast cancer is an important, real-world medical problem. In this article is introduced a new approach for diagnosis of breast cancer. The proposed approach uses Fuzzy c-means (FCM) algorithm and patter...

Fuzzy C-means (FCM) is a popular algorithm using the partitioning approach to solve problems in data clustering. A drawback to FCM, however, is that it requires the number of clusters and the clustering partition matrix to be set a priori. Typically, the former is set by the user and the latter is initialized randomly. This approach may cause the algorithm get stuck in a local optimum because F...

0957-4174/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.eswa.2010.07.112 ⇑ Corresponding author. E-mail addresses: [email protected] (H. I org (A. Abraham). Fuzzy clustering is an important problem which is the subject of active research in several real-world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient,...