A design methodology of interval type-2 fuzzy c-means clustering algorithm-based fuzzy inference systems (IT2FCMFIS) is introduced in this paper. An interval type-2 fuzzy c-means (IT2FCM) clustering algorithm is developed to generate the fuzzy rules in the form of the scatter partition of input space. And the individual partitioned spaces describe the fuzzy rules equal to the number of clusters...

Clustering algorithms are highly dependent on the features used and the type of the objects in a particular image. By considering object similar surface variations (SSV) as well as the arbitrariness of the fuzzy c-means (FCM) algorithm for pixel location, a fuzzy image segmentation considering object surface similarity (FSOS) algorithm was developed, but it was unable to segment objects having ...

Clustering algorithms are highly dependent on the features used and the type of the objects in a particular image. By considering object similar surface variations (SSV) as well as the arbitrariness of the fuzzy c-means (FCM) algorithm for pixel location, a fuzzy image segmentation considering object surface similarity (FSOS) algorithm was developed, but it was unable to segment objects having ...

background: in this paper we compare a highly accurate supervised to an unsupervised technique that uses breast thermal images with the aim of assisting physicians in early detection of breast cancer. methods: first, we segmented the images and determined the region of interest. then, 23 features that included statistical, morphological, frequency domain, histogram and gray-level co-occurrence ...

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).

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...

Mono-nuclear kernel function is presented in this paper based on the fuzzy c-means clustering algorithm for data clustering to do the improvement in the field of data mining, puts forward the fuzzy c-means clustering algorithm based on multiple kernel function (MKFCM) algorithm. Under fully unsupervised learning method, a set of Gaussian kernel function combination are assigned different weight...