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).
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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.
