A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data
Publish Year: 1399
Type: Journal paper
Language: English
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Document National Code:
JR_JADM-8-4_006
Index date: 10 May 2021
A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data abstract
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.
A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data Keywords:
A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data authors
J. Tayyebi
Department of Industrial Engineering, Birjand University of Technology, Birjand, Iran.
E. Hosseinzadeh
Department of Mathematics, Kosar University of Bojnord, Bojnord, Iran.
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