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Title: A string grammar possibilistic-fuzzy C-medians
Authors: Atcharin Klomsae
Sansanee Auephanwiriyakul
Nipon Theera-Umpon
Keywords: Computer Science
Issue Date: 1-Jan-2018
Abstract: © 2018, Springer-Verlag GmbH Germany, part of Springer Nature. In the context of syntactic pattern recognition, we adopt the fuzzy clustering approach to classify the syntactic pattern. A syntactic pattern can be described using a string grammar. Fuzzy clustering has been shown to have better performance than hard clustering. Previously, to improve the string grammar hard C-means, we introduced a string grammar fuzzy C-medians and string grammar fuzzy-possibilistic C-medians algorithm. However, both algorithms have their own problem. Thus, in this paper, we develop a string grammar possibilistic-fuzzy C-medians algorithm. The experiments on four real data sets show that string grammar possibilistic-fuzzy C-medians has better performance than string grammar hard C-means, string grammar fuzzy C-medians, and string grammar fuzzy-possibilistic C-medians. We claim that the proposed string grammar possibilistic-fuzzy C-medians is better than the other string grammar clustering algorithms.
ISSN: 14337479
Appears in Collections:CMUL: Journal Articles

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