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General Information
    • ISSN: 1793-8236 (Online)
    • Abbreviated Title Int. J. Eng. Technol.
    • Frequency:  Quarterly 
    • DOI: 10.7763/IJET
    • Executive Editor: Ms.Yoyo Y. Zhou
    • Abstracting/ Indexing: Chemical Abstracts Services (CAS) EBSCO, Google Scholar, Ulrich Periodicals Directory, Crossref, ProQuest, Index CopernicusEI (INSPEC, IET).
    • E-mail: ijet@vip.163.com
Prof. T. Hikmet Karakoc
Anadolu University, Faculty of Aeronautics and Astronautics, Turkey

IJET 2016 Vol.8(4): 301-306 ISSN: 1793-8236
DOI: 10.7763/IJET.2016.V8.902

An Overview of Semi-Supervised Fuzzy Clustering Algorithms

Pham Huy Thong and Le Hoang Son
Abstract—Fuzzy clustering plays an important role in data-mining, especially in decision making, pattern recognition, etc. There have been many approaches to improve fuzzy clustering performance and quality when it was first introduced by Bezdek. Recently, an approach related to data with sub-information has been most concerned. The idea of this approach combines the advantages of fuzzy C-means method with the benefits of additional information so-called the semi-supervised fuzzy clustering algorithms (SSFC). Through this report, a series of typical SSFC algorithms are presented in brief to give an overview of this approach.

Index Terms—Clustering performance, data mining, fuzzy clustering, fuzzy c-means, semi-supervised fuzzy clustering algorithm.

The authors are with VNU University of Science, Vietnam National University, Vietnam (e-mail: thongph@vnu.edu.vn, sonlh@vnu.edu.vn).


Cite: Pham Huy Thong and Le Hoang Son, "An Overview of Semi-Supervised Fuzzy Clustering Algorithms," International Journal of Engineering and Technology vol. 8, no. 4, pp. 301-306, 2016.

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