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A novel efficient clustering algorithm based on possibilistic approach and kernel technique for image clustering problems

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Abstract

In this paper, we proposed a new clustering algorithm named KGPFCM algorithm, based on the Generalized Possibilistic Fuzzy C-Means (GPFCM) algorithm and the Kernel method. The presented algorithm projects features in a nonlinear high dimensional input space into a high-dimensional linear output space, which preserves the locality of structural properties, thus improving the ability of the GPFCM algorithm to handle high-dimensional data. However. In addition, KGPFCM algorithm uses in addition to the Euclidean distance, further powerful norms more adapted to various complex problems. Moreover, compared to PFCM, GPFCM and two other recent algorithms like RSFCM and LPFCM methods, KGPFCM algorithm corrects many shortcomings, and the use of Kernel method provides a solution to the high dimensional space and the problem of nonlinear separable input space, hence, KGPFCM is regarded as a unified model that combines two important approaches in clustering technique. Therefore, we applied the KGPFCM algorithm as a new image clustering method based on the kernel technique and we utilized Jacobi orthogonal moments to extract the feature vectors from the images. Experimental results on some benchmark data sets show the effectiveness of KGPFCM algorithm in comparison with GPFCM and some state-of-the-art methods to detect cluster centers accurately and to give satisfactory results in image clustering field.

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Data availability

This manuscript has associated real data in data UCI Machine Learning Repository, https://archive.ics.uci.edu/ml/index.php.

The data used to support the findings of this study are available in: Coil-20: https://www.cs.columbia.edu/CAVE/software/softlib/coil-20.php.

MPEG-7-CE: https://dabi.temple.edu/external/shape/MPEG7/dataset.html.

ORL: https://cam-orl.co.uk/facedatabase.html.

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Authors and Affiliations

  1. Mechanical Engineering Laboratory, Faculty of Sciences and Techniques, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Souad Azzouzi & Ahmed EL Khalfi

  2. LISAC, Faculty of Sciences Dhar El Mehraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Amal Hjouji

  3. TI Laboratory, High School of Technology, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Jaouad EL-Mekkaoui

Authors
  1. Souad Azzouzi
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  2. Amal Hjouji
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  3. Jaouad EL-Mekkaoui
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  4. Ahmed EL Khalfi
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Correspondence to Souad Azzouzi.

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Azzouzi, S., Hjouji, A., EL-Mekkaoui, J. et al. A novel efficient clustering algorithm based on possibilistic approach and kernel technique for image clustering problems. Appl Intell 53, 4327–4349 (2023). https://doi.org/10.1007/s10489-022-03703-0

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