This paper presents an algorithm, called the modified suppressed fuzzy c-means (MS-FCM), that simultaneously performs clustering and parameter selection for the suppressed fuzzy c-means (S-FCM) algorithm proposed by [Fan, J.L., Zhen, W.Z., Xie, W.X., 2003. Suppressed fuzzy c-means clustering algorithm. Pattern Recognition Lett. 24, 1607–1612]. The proposed algorithm is computationally simple, a...

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...

An improved fuzzy c-means algorithm is put forward and applied to deal with meteorological data on top of the traditional fuzzy c-means algorithm. The proposed algorithm improves the classical fuzzy c-means algorithm (FCM) by adopting a novel strategy for selecting the initial cluster centers, to solve the problem that the traditional fuzzy c-means (FCM) clustering algorithm has difficulty in s...

nThis paper transmits a FORTRAN-IV coding of the fuzzy c-means (FCM) clustering program. The FCM program is applicable to a wide variety of geostatistical data analysis problems. This program generates fuzzy partitions and prototypes for any set of numerical data. These partitions are useful for corroborating known substructures or suggesting substructure in unexplored data. The clustering crit...

this paper presents an efficient hybrid method, namely fuzzy particleswarm optimization (fpso) and fuzzy c-means (fcm) algorithms, to solve the fuzzyclustering problem, especially for large sizes. when the problem becomes large, thefcm algorithm may result in uneven distribution of data, making it difficult to findan optimal solution in reasonable amount of time. the pso algorithm does find ago...

This study aims to empirically analyze teaching-learning-based optimization (TLBO) and machine learning algorithms using k-means fuzzy c-means (FCM) for their individual performance evaluation in terms of clustering classification. In the first phase, (k-means FCM) were employed independently accuracy was evaluated different computational measures. During second non-clustered data obtained from...