Fuzzy C-means (FCM) is an important clustering algorithm with broad applications such as retail market data analysis, network monitoring, web usage mining, and stock prediction. Especially, parameters in FCM have influence on results. However, a lot of did not solve the problem, that is, how to set parameters. In this study, we present kind method for computing values according role process. Ne...

In fuzzy clustering, the fuzzy c-means (FCM) algorithm is the most commonly used clustering method. However, the FCM algorithm is usually affected by initializations. Incorporating FCM into switching regressions, called the fuzzy c-regressions (FCR), has also the same drawback as FCM, where bad initializations may cause difficulties in obtaining appropriate clustering and regression results. In...

Interpretation of MRI images is difficult due to inherent noise and inhomogeneity. Segmentation is considered as vitally important step in medical image analysis and classification. Several methods are employed for medical image segmentation such as clustering method, thresholding method, region growing etc. In this paper, attention has been focused on clustering method such as Fuzzy C-means cl...

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...

This paper presents a two-phase clustering algorithm for machine-cell and partfamily formation in the design of cellular manufacturing systems. The proposed algorithm begins with the determination of initial cluster centers via a linear assignment method using the least similar group representatives in its first phase. A fuzzy C-means clustering method is followed in its second phase for part-f...