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A Fitness-Based Adaptive Differential Evolution Approach to Data Clustering

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Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 199))

Abstract

Fuzzy clustering helps to find natural vague boundaries in data. The fuzzy c-means (FCM) is one of the most popular clustering methods based on minimization of a criterion function as it works fast in most scenarios. However, it is sensitive to initialization and is easily trapped in local optima. In this work, a fuzzy clustering (FC) algorithm based on Differential Evolution (DE) is proposed. Here we use a DE with Fitness Based Adaptive Technique (FBADE) for the adaptation of DE parameters. 3 well-known data sets viz. Iris, Wine, Motorcycle and 2 synthetic datasets are used to demonstrate the effectiveness of the algorithm. The resulting algorithm is compared with conventional Fuzzy C-Means (FCM) algorithm, FCM with DE (FCM-DE), FCM with Self Adaptive DE (FCM-SADE).

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Authors and Affiliations

  1. Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, 700032, India

    G. R. Patra, T. Singha & S. S. Choudhury

  2. Engineering and Communication Sciences Unit, Indian Statistical Institute, Kolkata, 700108, India

    S. Das

Authors
  1. G. R. Patra
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  2. T. Singha
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  3. S. S. Choudhury
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  4. S. Das
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Corresponding author

Correspondence to G. R. Patra .

Editor information

Editors and Affiliations

  1. Dept of Computer Science Engineering, Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, India

    Suresh Chandra Satapathy

  2. AI Lab, University of Hyderabad, Hyderabad, India

    Siba K. Udgata

  3. Bhubaneswar Engineering College, Bhubaneswar, India

    Bhabendra Narayan Biswal

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Patra, G.R., Singha, T., Choudhury, S.S., Das, S. (2013). A Fitness-Based Adaptive Differential Evolution Approach to Data Clustering. In: Satapathy, S., Udgata, S., Biswal, B. (eds) Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA). Advances in Intelligent Systems and Computing, vol 199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35314-7_53

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  • DOI: https://doi.org/10.1007/978-3-642-35314-7_53

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35313-0

  • Online ISBN: 978-3-642-35314-7

  • eBook Packages: EngineeringEngineering (R0)

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