A Learning Scheme to Fuzzy C-Means based on a Compromise in Updating Membership Degrees

Shang-Lin Wu, Yang-Yin Lin, Yu-Ting Liu, Chih-Yu Chen, Chin-Teng Lin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.
Original languageEnglish
Title of host publication2014 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)
PublisherIEEE
Pages1534-1537
Number of pages4
DOIs
StatePublished - 2014

Keywords

  • Fuzzy C-Means (FCM); Clustering; Data classification; High computational complexity; Long execution time

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