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dc.contributor.authorWu, Shang-Linen_US
dc.contributor.authorLin, Yang-Yinen_US
dc.contributor.authorLiu, Yu-Tingen_US
dc.contributor.authorChen, Chih-Yuen_US
dc.contributor.authorLin, Chin-Tengen_US
dc.date.accessioned2015-07-21T08:30:53Z-
dc.date.available2015-07-21T08:30:53Z-
dc.date.issued2014-01-01en_US
dc.identifier.isbn978-1-4799-2072-3en_US
dc.identifier.issn1544-5615en_US
dc.identifier.urihttp://hdl.handle.net/11536/125050-
dc.description.abstractFuzzy C-Means (FCM) clustering is the most well-known clustering method according to fuzzy partition for pattern classification. However, there are some disadvantages of using that clustering method, such as computational complexity and execution time. Therefore, to solve these drawbacks of FCM, the two-phase FCM procedure has been proposed in this study. Compared with the conventional FCM, the usage of a compromised learning scheme makes more adaptive and effective. By performing the proposed approach, the unknown data could be rapidly clustered according to the previous information. A synthetic data set with two dimensional variables is generated to estimate the performance of the proposed method, and to further demonstrate that our method not only reduces computational complexity but economizes execution time compared with the conventional FCM in each example.en_US
dc.language.isoen_USen_US
dc.subjectFuzzy C-Means (FCM)en_US
dc.subjectClusteringen_US
dc.subjectData classificationen_US
dc.subjectHigh computational complexityen_US
dc.subjectLong execution timeen_US
dc.titleA Learning Scheme to Fuzzy C-Means based on a Compromise in Updating Membership Degreesen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2014 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)en_US
dc.citation.spage1534en_US
dc.citation.epage1537en_US
dc.contributor.department分子醫學與生物工程研究所zh_TW
dc.contributor.department電控工程研究所zh_TW
dc.contributor.department腦科學研究中心zh_TW
dc.contributor.departmentInstitute of Molecular Medicine and Bioengineeringen_US
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.contributor.departmentBrain Research Centeren_US
dc.identifier.wosnumberWOS:000350793500221en_US
dc.citation.woscount0en_US
Appears in Collections:Conferences Paper