Paper Info
Title
Analysis and efficient implementation of a linguistic fuzzy c-means
Abstract
The paper is concerned with a linguistic fuzzy c-means (FCM) algorithm with vectors of fuzzy numbers as inputs. This algorithm is based on the extension principle and the decomposition theorem. It turns out that using the extension principle to extend the capability of the standard membership update equation to deal with a linguistic vector has a huge computational complexity. In order to cope with this problem, an efficient method based on fuzzy arithmetic and optimization has been developed and analyzed. We also carefully examine and prove that the algorithm behaves in a way similar to the FCM in the degenerate linguistic case. Synthetic data sets and the iris data set have been used to illustrate the behavior of this linguistic version of the FCM.
Year
DOI
Venue
2002
10.1109/TFUZZ.2002.803492
IEEE T. Fuzzy Systems
Keywords
Field
DocType
computational complexity,fuzzy logic,fuzzy set theory,pattern recognition,vectors,computational complexity,decomposition theorem,extension principle,fuzzy arithmetic,fuzzy numbers,linguistic fuzzy c-means algorithm,linguistic vectors,optimization
Degenerate energy levels,Fuzzy logic,Fuzzy set,Artificial intelligence,Iris flower data set,Type-2 fuzzy sets and systems,Cluster analysis,Fuzzy number,Linguistics,Machine learning,Mathematics,Computational complexity theory
Journal
Volume
Issue
ISSN
10
5
1063-6706
Citations 
PageRank 
References 
21
1.70
11
Authors
2
Name
Order
Citations
PageRank
S. Auephanwiriyakul124639.45
James M. Keller23201436.69