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An Intuitionistic Fuzzy Approach to Fuzzy Clustering of Numerical Dataset

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Computational Intelligence, Cyber Security and Computational Models

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 246))

Abstract

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.

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Authors and Affiliations

  1. Kongu Engineering College, Perundurai, Erode, Tamil Nadu, India

    N. Karthikeyani Visalakshi & S. Parvathavarthini

  2. Periyar University, Salem, Tamil Nadu, India

    K. Thangavel

Authors
  1. N. Karthikeyani Visalakshi
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  2. S. Parvathavarthini
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  3. K. Thangavel
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Corresponding author

Correspondence to N. Karthikeyani Visalakshi .

Editor information

Editors and Affiliations

  1. Applied Mathematics and Computational Sciences, PSG College of Technology, Coimbatore, Tamil Nadu, India

    G. Sai Sundara Krishnan

  2. Applied Mathematics and Computational Sciences, PSG College of Technology, Coimbatore, Tamil Nadu, India

    R. Anitha

  3. Applied Mathematics and Computational Sciences, PSG College of Technology, Coimbatore, Tamil Nadu, India

    R. S. Lekshmi

  4. Applied Mathematics and Computational Sciences, PSG College of Technology, Coimbatore, Tamil Nadu, India

    M. Senthil Kumar

  5. Department of Mathematics, Ryerson University, Toronto, Ontario, Canada

    Anthony Bonato

  6. University of Basque Country, Paseo Manuel De Lardizalbal 1, San Sebastian, Spain

    Manuel Graña

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Karthikeyani Visalakshi, N., Parvathavarthini, S., Thangavel, K. (2014). An Intuitionistic Fuzzy Approach to Fuzzy Clustering of Numerical Dataset. In: Krishnan, G., Anitha, R., Lekshmi, R., Kumar, M., Bonato, A., Graña, M. (eds) Computational Intelligence, Cyber Security and Computational Models. Advances in Intelligent Systems and Computing, vol 246. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1680-3_9

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  • DOI: https://doi.org/10.1007/978-81-322-1680-3_9

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-1679-7

  • Online ISBN: 978-81-322-1680-3

  • eBook Packages: EngineeringEngineering (R0)

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