Lim, K.M. and Loo, C.K. and Lim, W.S. (2010) Autonomous and deterministic supervised fuzzy clustering. Neural Network World, 20 (6). pp. 705-721. ISSN 1210-0552,
Full text not available from this repository.Abstract
A fuzzy model based on an enhanced supervised fuzzy clustering algorithm is presented in this paper. The supervised fuzzy clustering algorithm 6 allows each rule to represent more than one output with different probabilities for each output. This algorithm implements k-means to initialize the fuzzy model. However, the main drawbacks of this approach are that the number of clusters is unknown and the initial positions of clusters are randomly generated. In this work, the initialization is done by the global k-means algorithm 1, which can autonomously determine the actual number of clusters needed and give a deterministic clustering result. In addition, the fast global k-means algorithm 1 is presented to improve the computation time. The model is tested on medical diagnosis benchmark data and Westland vibration data. The results obtained show that the model that uses the global k-means clustering algorithm 1 has higher accuracy when compared to a model that uses the k-means clustering algorithm. Besides that, the fast global k-means algorithm 1 also improved the computation time without degrading much the model performance.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Additional Information: | Lim, Kian Ming Loo, Chu Kiong Lim, Way Soong |
| Uncontrolled Keywords: | Fuzzy model; fuzzy clustering algorithm; |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Artificial Intelligence |
| Depositing User: | Miss Nur Jannatul Adnin Ahmad Shafawi |
| Date Deposited: | 19 Mar 2013 01:20 |
| Last Modified: | 19 Mar 2013 01:20 |
| URI: | http://eprints.um.edu.my/id/eprint/5173 |
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