A string grammar possibilistic-fuzzy C-medians

• Main Authors: Atcharin Klomsae, Sansanee Auephanwiriyakul, Nipon Theera-Umpon
• Format: Journal
• Published: 2018
• Subjects: Computer Science; Mathematics
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85050299386&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/58557
• Institution: Chiang Mai University

© 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.