%0 Journal Article
%T A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data
%J Journal of AI and Data Mining
%I Shahrood University of Technology
%Z 2322-5211
%A Tayyebi, J.
%A Hosseinzadeh, E.
%D 2020
%\ 11/01/2020
%V 8
%N 4
%P 515-523
%! A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data
%K Fuzzy c-means algorithm
%K Incomplete data
%K Fuzzy data
%K Ranking function
%R 10.22044/jadm.2020.9021.2038
%X The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal fuzzy data. Then, as an application, a method based on the proposed algorithm is presented to cluster incomplete fuzzy data. The method substitutes missing attribute by a trapezoidal fuzzy number to be determined by using the corresponding attribute of q nearest-neighbor. Comparisons and analysis of the experimental results demonstrate the capability of the proposed method.
%U http://jad.shahroodut.ac.ir/article_1835_f448d9fb97ac8ee1d42df537c700271b.pdf