In order to make up some deficiencies of the fuzzy c-means clustering algorithm, a new FCM algorithm based on pretreatment of similarity relation between samples is proposed in the paper, which is utilized to estimate the fuzzy clustering centers and the weight coefficient of samples effecting on the fuzzy clustering centers during iteration process. The new FCM algorithm makes the clustering q...

Data deals with the specific problem of partitioning a group of objects into a fixed number of subsets, so that the similarity of the objects in each subset is increased and the similarity across subsets is reduced. Several algorithms have been proposed in the literature for clustering, where k-means clustering and Fuzzy C-Means (FCM) clustering are the two popular algorithms for partitioning t...

Fuzzy C-means (FCM) is an unsupervised clustering technique and is often used for the unsupervised segmentation of multivariate images. In traditional FCM the clustering is based on spectral information only and geometrical relationship between neighbouring pixels is not used in the clustering procedure. In this paper, the Spatially Guided FCM (SGFCM) algorithm is presented which segments multi...

Soft Clustering plays a very important rule on clustering real world data where a data item contributes to more than one cluster. Fuzzy logic based algorithms are always suitable for performing soft clustering tasks. Fuzzy C Means (FCM) algorithm is a very popular fuzzy logic based algorithm. In case of fuzzy logic based algorithm, the parameter like exponent for the partition matrix that we ha...

Horizontal collaborative clustering is such a clustering method that carries clustering on one data set describing a pattern set in one feature space with collaborative introducing of outer partition information obtained by clustering on another data set but describing the same pattern set in another feature space. In order to implement the collaborative clustering, horizontal collaborative fuz...

This article presents a study of academic performance evaluation using soft computing techniques inspired by the successful application of K-means, fuzzy C-means (FCM), subtractive clustering (SC), hybrid subtractive clustering-fuzzy C-means (SC-FCM) and hybrid subtractive clustering-adaptive neuro fuzzy inference system (SC-ANFIS) methods for solving academic performance evaluation problems. M...

Wujiang River, one of the main branches of the Beijiang River in South China, is frequently suffered from flood disasters. Flood clustering becomes one of the critical sub-issues for realizing the different types of flood features. This paper attempts to put forward a flood clustering approach for flood feature identification which is important to the flood risk management and flood forecasting...

In medical applications it is very important for a physician to be informed of patient situation as soon as possible especially in emergency circumstances. Therefore all efficient agents in patient health must be fast even medical algorithms such as clustering ones. Among clustering methods Fuzzy C-Means (FCM) clustering has been frequently used for segmentation of medical images. In this paper...

fuzzy clustering methods are conveniently employed in constructing a fuzzy model of a system, but they need to tune some parameters. in this research, fcm is chosen for fuzzy clustering. parameters such as the number of clusters and the value of fuzzifier significantly influence the extent of generalization of the fuzzy model. these two parameters require tuning to reduce the overfitting in the...