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

doi:10.1109/ICMLC.2011.6016708

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

Department of Biology

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
Pages	199-203
Number of pages	5
Volume	1
DOIs	https://doi.org/10.1109/ICMLC.2011.6016708
Publication status	Published - 2011 Nov 7
Event	2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011 - Guilin, Guangxi, China Duration: 2011 Jul 10 → 2011 Jul 13

Other

Other	2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
Country	China
City	Guilin, Guangxi
Period	11-07-10 → 11-07-13

All Science Journal Classification (ASJC) codes

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

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Cite this

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title = "An extensional fuzzy c-means clustering algorithm based on intuitionistic extension index",

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.",

author = "Liu, {Hsiang Chuan} and Yu, {Yen Kuei} and Hsien-Chang Tsai and Liu, {Tung Sheng} and Jeng, {Bai Cheng}",

year = "2011",

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Liu, HC, Yu, YK, Tsai, H-C, Liu, TS & Jeng, BC 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, 6016708, pp. 199-203, 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011, Guilin, Guangxi, China, 11-07-10. https://doi.org/10.1109/ICMLC.2011.6016708

An extensional fuzzy c-means clustering algorithm based on intuitionistic extension index. / Liu, Hsiang Chuan; Yu, Yen Kuei; Tsai, Hsien-Chang; Liu, Tung Sheng; Jeng, Bai Cheng.

Proceedings of 2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011. Vol. 1 2011. p. 199-203 6016708.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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