An extensional fuzzy c-means clustering algorithm based on intuitionistic extension index

Hsiang Chuan Liu, Yen Kuei Yu, Hsien-Chang Tsai, Tung Sheng Liu, Bai Cheng Jeng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, a novel fuzzy c-means algorithm based on an intuitionistic extension index for any n-dimensional point set, namely the E-FCM algorithm, is being proposed. If the intuitionistic extension index is equal to 0, then the proposed new algorithm is just the traditional fuzzy c-means algorithm (FCM), in other words, the E-FCM algorithm is a generalization of the FCM algorithm. It is quite different from Xu and Wu's intuitionistic fuzzy C-means clustering algorithm (IFCM algorithm), since the latter can only be used for intuitionistic fuzzy sets, but not for any n-dimensional point set. The experimental results of three benchmark data sets show that the proposed E-FCM algorithm outperforms the FCM algorithm.

Original languageEnglish
Title of host publicationProceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
Pages199-203
Number of pages5
Volume1
DOIs
Publication statusPublished - 2011 Nov 7
Event2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 - Guilin, Guangxi, China
Duration: 2011 Jul 102011 Jul 13

Other

Other2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
CountryChina
CityGuilin, Guangxi
Period11-07-1011-07-13

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Human-Computer Interaction

Cite this

Liu, H. C., Yu, Y. K., Tsai, H-C., Liu, T. S., & Jeng, B. C. (2011). An extensional fuzzy c-means clustering algorithm based on intuitionistic extension index. In Proceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 (Vol. 1, pp. 199-203). [6016708] https://doi.org/10.1109/ICMLC.2011.6016708