Analysis and efficient implementation of a linguistic fuzzy C-means
This paper is concerned with a linguistic fuzzy C-means (FCM) algorithm with vectors of fuzzy numbers as inputs. This algorithm is based on the extension principles and the decomposition theorem. It turns out that using the extension principle to extend the capability of the standard membership upda...
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th-cmuir.6653943832-13652014-08-29T09:29:13Z Analysis and efficient implementation of a linguistic fuzzy C-means Auephanwiriyakul S. Keller J.M. This paper is concerned with a linguistic fuzzy C-means (FCM) algorithm with vectors of fuzzy numbers as inputs. This algorithm is based on the extension principles and the decomposition theorem. It turns out that using the extension principle to extend the capability of the standard membership update equation to deal with a linguistic vector has a huge computational complexity. In order to cope with this problem, an efficient method based on fuzzy arithmetic and optimization has been developed and analyzed. We also carefully examine and prove that the algorithm behaves in a way similar to the FCM in the degenerate linguistic case. Synthetic data sets and the iris data set have been used to illustrate the behavior of this linguistic version of the FCM. 2014-08-29T09:29:13Z 2014-08-29T09:29:13Z 2002 Article 10636706 10.1109/TFUZZ.2002.803492 IEFSE http://www.scopus.com/inward/record.url?eid=2-s2.0-0036802259&partnerID=40&md5=71ef7671c5926787edc526fca285261b http://cmuir.cmu.ac.th/handle/6653943832/1365 English |
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This paper is concerned with a linguistic fuzzy C-means (FCM) algorithm with vectors of fuzzy numbers as inputs. This algorithm is based on the extension principles and the decomposition theorem. It turns out that using the extension principle to extend the capability of the standard membership update equation to deal with a linguistic vector has a huge computational complexity. In order to cope with this problem, an efficient method based on fuzzy arithmetic and optimization has been developed and analyzed. We also carefully examine and prove that the algorithm behaves in a way similar to the FCM in the degenerate linguistic case. Synthetic data sets and the iris data set have been used to illustrate the behavior of this linguistic version of the FCM. |
| format |
Article |
| author |
Auephanwiriyakul S. Keller J.M. |
| spellingShingle |
Auephanwiriyakul S. Keller J.M. Analysis and efficient implementation of a linguistic fuzzy C-means |
| author_facet |
Auephanwiriyakul S. Keller J.M. |
| author_sort |
Auephanwiriyakul S. |
| title |
Analysis and efficient implementation of a linguistic fuzzy C-means |
| title_short |
Analysis and efficient implementation of a linguistic fuzzy C-means |
| title_full |
Analysis and efficient implementation of a linguistic fuzzy C-means |
| title_fullStr |
Analysis and efficient implementation of a linguistic fuzzy C-means |
| title_full_unstemmed |
Analysis and efficient implementation of a linguistic fuzzy C-means |
| title_sort |
analysis and efficient implementation of a linguistic fuzzy c-means |
| publishDate |
2014 |
| url |
http://www.scopus.com/inward/record.url?eid=2-s2.0-0036802259&partnerID=40&md5=71ef7671c5926787edc526fca285261b http://cmuir.cmu.ac.th/handle/6653943832/1365 |
| _version_ |
1681419657030926336 |