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Model-based fuzzy c-shells clustering

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Abstract

In this paper, a new shell clustering method is presented to cluster model-based shells in 2-dimensions. Shells, that each one of them can be expressed by a center and non-negative radius in each angle (by considering polar coordinate system), can cluster by using the proposed model-based fuzzy c-shells (MFCS) clustering method. In this paper, firstly one of the most famous traditionally clustering methods, i.e. fuzzy c-spherical shells (FCSS) clustering method, is extracted from the proposed MFCS clustering method as a specific state of it. Then, the performance of proposed method is examined in three examples when it is applied over shells with various shapes in 2-dimensions. Since the resulted systems of equations in the studied examples cannot be solved directly, the particle swarm optimization (PSO) algorithm is used to numerically solve the resulted equations systems. The simulation results show the acceptable performance of the proposed MFCS method.

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Authors and Affiliations

  1. Electrical Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran

    Hadi Hossein-Abad Mahdipour & Morteza Khademi

  2. Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran

    Hadi Yazdi Sadoghi

Authors
  1. Hadi Hossein-Abad Mahdipour
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  2. Morteza Khademi
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  3. Hadi Yazdi Sadoghi
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Correspondence to Hadi Yazdi Sadoghi.

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Hossein-Abad Mahdipour, H., Khademi, M. & Yazdi Sadoghi, H. Model-based fuzzy c-shells clustering. Neural Comput & Applic 21 (Suppl 1), 29–41 (2012). https://doi.org/10.1007/s00521-011-0571-0

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  • DOI: https://doi.org/10.1007/s00521-011-0571-0

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