A string grammar possibilistic-fuzzy C-medians

Abstract

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.

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Acknowledgements

The authors would like to thank Thailand Research Fund and ChiangMai University under the Royal Golden Jubilee Ph.D. Program (Grant no. PHD/0044/2555) for financial support.

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Correspondence to Sansanee Auephanwiriyakul.

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Klomsae, A., Auephanwiriyakul, S. & Theera-Umpon, N. A string grammar possibilistic-fuzzy C-medians. Soft Comput 23, 7637–7653 (2019). https://doi.org/10.1007/s00500-018-3392-6

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Keywords

  • Fuzzy median
  • String grammar possibilistic-fuzzy c-medians
  • Levenshtein distance
  • Syntactic pattern recognition