An Intuitionistic Fuzzy Approach to Fuzzy Clustering of Numerical Dataset

  • N. Karthikeyani Visalakshi
  • S. Parvathavarthini
  • K. Thangavel
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 246)


Fuzzy c-means (FCM) clustering is one of the most widely used fuzzy clustering algorithms. However, the main disadvantage of this algorithm is its sensitivity to noise and outliers. Intuitionistic fuzzy set is a suitable tool to cope with imperfectly defined facts and data, as well as with imprecise knowledge. So far, there exists a little investigation on FCM algorithm for clustering intuitionistic fuzzy data. This paper focuses mainly on two aspects. Firstly, it proposes an intuitionistic fuzzy representation (IFR) scheme for numerical dataset and applies the modified FCM clustering for clustering intuitionistic fuzzy (IF) data and comparing results with that of crisp and fuzzy data. Secondly, in clustering of IF data, different IF similarity measures are studied and a comparative analysis is carried out on the results. The experiments are conducted for numerical datasets of UCI machine learning data repository.


Clustering Fuzzy c-means Intuitionistic fuzzy data Intuitionistic fuzzy similarity measure 


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Copyright information

© Springer India 2014

Authors and Affiliations

  • N. Karthikeyani Visalakshi
    • 1
  • S. Parvathavarthini
    • 1
  • K. Thangavel
    • 2
  1. 1.Kongu Engineering CollegePerundurai, ErodeIndia
  2. 2.Periyar UniversitySalemIndia

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