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A New Rough-Fuzzy Clustering Algorithm and its Applications

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Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 236))

Abstract

Cluster analysis is a technique that divides a given data set into a set of clusters in such a way that two objects from the same cluster are as similar as possible and the objects from different clusters are as dissimilar as possible. A robust rough-fuzzy \(c\)-means clustering algorithm is applied here to identify clusters having similar objects. Each cluster of the robust rough-fuzzy clustering algorithm is represented by a set of three parameters, namely, cluster prototype, a possibilistic fuzzy lower approximation, and a probabilistic fuzzy boundary. The possibilistic lower approximation helps in discovering clusters of various shapes. The cluster prototype depends on the weighting average of the possibilistic lower approximation and probabilistic boundary. The reported algorithm is robust in the sense that it can find overlapping and vaguely defined clusters with arbitrary shapes in noisy environment. The effectiveness of the clustering algorithm, along with a comparison with other clustering algorithms, is demonstrated on synthetic as well as coding and non-coding RNA expression data sets using some cluster validity indices.

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Acknowledgments

This work is partially supported by the Indian National Science Academy, New Delhi (Grant No. SP/YSP/68/2012). The work was done when one of the authors, S. Paul, was a Senior Research Fellow of Council of Scientific and Industrial Research, Government of India.

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Authors and Affiliations

  1. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

    Sushmita Paul & Pradipta Maji

Authors
  1. Sushmita Paul
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  2. Pradipta Maji
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Corresponding author

Correspondence to Sushmita Paul .

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Editors and Affiliations

  1. Institute of Engineering and Technology, JK Lakshmipat University, Jaipur, Rajasthan, India

    B. V. Babu

  2. Department of Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya Nagar

  3. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  4. Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, India

    Millie Pant

  5. Department of Applied Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  6. Institute of Engineering and Technology, JK Lakshmipat University, Jaipur, Rajasthan, India

    Kanad Ray

  7. Institute of Engineering and Technology, JK Lakshmipat University, Jaipur, Rajasthan, India

    Umesh Gupta

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Paul, S., Maji, P. (2014). A New Rough-Fuzzy Clustering Algorithm and its Applications. In: Babu, B., et al. Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012. Advances in Intelligent Systems and Computing, vol 236. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1602-5_130

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  • DOI: https://doi.org/10.1007/978-81-322-1602-5_130

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-1601-8

  • Online ISBN: 978-81-322-1602-5

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