標題: A Learning Scheme to Fuzzy C-Means based on a Compromise in Updating Membership Degrees
作者: Wu, Shang-Lin
Lin, Yang-Yin
Liu, Yu-Ting
Chen, Chih-Yu
Lin, Chin-Teng
分子醫學與生物工程研究所
電控工程研究所
腦科學研究中心
Institute of Molecular Medicine and Bioengineering
Institute of Electrical and Control Engineering
Brain Research Center
關鍵字: Fuzzy C-Means (FCM);Clustering;Data classification;High computational complexity;Long execution time
公開日期: 1-Jan-2014
摘要: Fuzzy 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.
URI: http://hdl.handle.net/11536/125050
ISBN: 978-1-4799-2072-3
ISSN: 1544-5615
期刊: 2014 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)
起始頁: 1534
結束頁: 1537
Appears in Collections:Conferences Paper