sis 20(24): e1

Research Article

A new Semi-Supervised Intuitionistic Fuzzy C-means Clustering

Download626 downloads
  • @ARTICLE{10.4108/eai.13-7-2018.159622,
        author={J. Arora and M.  Tushir},
        title={A new Semi-Supervised Intuitionistic Fuzzy C-means Clustering},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={7},
        number={24},
        publisher={EAI},
        journal_a={SIS},
        year={2019},
        month={7},
        keywords={Semi-Supervised Clustering, Intuitionistic Fuzzy-Set, Image Segmentation},
        doi={10.4108/eai.13-7-2018.159622}
    }
    
  • J. Arora
    M. Tushir
    Year: 2019
    A new Semi-Supervised Intuitionistic Fuzzy C-means Clustering
    SIS
    EAI
    DOI: 10.4108/eai.13-7-2018.159622
J. Arora1,*, M. Tushir2
  • 1: Assistant Professor, Dept. of Information Technology, MSIT, Affiliated to GGSIPU, Delhi, India
  • 2: Professor, Dept. of Electrical & Electronics Engineering, MSIT, Affiliated to GGSIPU, Delhi, India
*Contact email: joy.arora@gmail.com

Abstract

Semi-supervised clustering algorithms aim to increase the accuracy of unsupervised clustering process by effectively exploring the limited supervision available in the form of labelled data. Also the intuitionistic fuzzy sets, a generalization of fuzzy sets, have been proven to deal better with the problem of uncertainty present in the data. In this paper, we have proposed to embed the concept of intuitionistic fuzzy set theory with semi-supervised approach to further improve the clustering process. We evaluated the performance of the proposed methodology on several benchmark real data sets based on several internal and external indices. The proposed Semi-Supervised Intuitionistic Fuzzy C-means clustering is compared with several state of the art clustering/classification algorithms. Experimental results show that our proposed algorithm is a better alternative to these competing approaches.