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Please use this identifier to cite or link to this item:
https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/38703
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| Title: | K-means-based fuzzy classifier design |
| Authors: | 翁慶昌;Wong, Ching-chang;Chen, Chia-chong;Yeh, Shih-liang |
| Contributors: | 淡江大學電機工程學系 |
| Date: | 2000-05-07 |
| Issue Date: | 2010-04-15 11:35:38 (UTC+8) |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| Abstract: | In this paper, a method based on the K-means algorithm is proposed to efficiently design a fuzzy classifier so that the training patterns can be correctly classified by the proposed approach. In this method, the K-means algorithm is first used to partition the training data for each class into several clusters, and the cluster center and the radius for each cluster are calculated. Then, a fuzzy system design method that uses a fuzzy rule to represent a cluster is proposed such that a fuzzy classifier can be efficiently constructed to correctly classify the training data. The proposed method has the following features: 1) it does not need prior parameter definition; 2) it only needs a short training time; and 3) it is simple. Finally, two examples are used to illustrate and examine the proposed method for the fuzzy classifier design |
| Relation: | Fuzzy Systems, 2000. FUZZ IEEE 2000. The Ninth IEEE International Conference on (Volume:1 ), pp.48-52 |
| DOI: | 10.1109/FUZZY.2000.838632 |
| Appears in Collections: | [電機工程學系暨研究所] 會議論文
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Files in This Item:
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Size | Format | |
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| 0780358775_1p48-52.pdf | | 351Kb | Adobe PDF | 843 | View/Open | | index.html | | 0Kb | HTML | 454 | View/Open |
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