Entropy assessment for type-2 fuzziness

Fuzzy functions with support vector machines

“…It should be noted that the entropy assessment of membership values in [18] is used to determine the uncertainty interval of fuzzy models. In this paper, we used the same measure to find the optimum parameters of the FCM algorithm.…”

“…. , 10 and validate each model with a cluster validity index [18,19]. (iii) Using the optimal pairs, (m * , c * ), of the FCM models and the cluster centers of the optimum model,…”

A fuzzy analysis of country-size argument for the Feldstein–Horioka puzzle

“…There are at least two advantages of FCM that attract researchers: (i) its power of linguistic explanation with resulting ease of understanding, and (ii) its tolerance to imprecise data which provides flexibility and stability for classification and prediction. Because of these features, FCM has been increasingly applied to problems in various areas such as computer science, system analysis, electronic engineering, pharmacology, finance and more recently social sciences (some related examples are [1,5,8,22,[24][25][26]28]). For instance, clustering of the countries based on their relative sizes lead to groups such as, ''small", ''medium", ''large" and ''very large" countries.…”

Implementation of the Fuzzy C-Means Clustering Algorithm in Meteorological Data

“…The other way to deal with the parameter m is realizing the management of uncertainty on the basis of the fuzziness index. I. Ozkan and I. Turksen [28] introduced a approach that evaluate m according to entropies after removing uncertainties from all other parameters. C. Hwang et al, [29] incorporated the interval type-2 fuzzy set into the FCM algorithm to manage the uncertainty for fuzziness index m .…”