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 |
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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 | |
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 |
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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