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Enhanced Grey Wolf Optimizer for Data Clustering

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Artificial Intelligence: Theories and Applications (ICAITA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1769))

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Abstract

Data clustering is an unsupervised learning method used to extract knowledge from data, it is an NP-Hard (nondeterministic polynomial time) problem; there is no known deterministic technique that can find the optimal solution with an appropriate time complexity. Metaheuristics are powerful tools used to find good solutions (near to the best one) in a feasible time. The objective of this work is to improve the quality of one of recent metaheuristic clustering-based algorithms, which is grey wolf optimizer metaheuristic (GWO) by proposing an enhanced version of GWO called Enhanced Grey Wolf Algorithm-based Clustering (EGWAC), GWO is applied to find the best cluster centers. The optimization is essentially done in the updation of wolves position. The assessment of the results is measured by three measures; precision, recall and G-measure. The enhanced version of GWO algorithm for data clustering showed the impressive effect of the optimizations.

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Author information

Authors and Affiliations

  1. Department of Computer Science, LICUS Laboratory, Université 20 Août 1955, 21000, Skikda, Algeria

    Ibrahim Zebiri, Djamel Zeghida & Mohammed Redjimi

  2. Department of Computer Science, LISCO Laboratory, University Badji Mokhtar, 23000, Annaba, Algeria

    Djamel Zeghida

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  1. Ibrahim Zebiri
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  2. Djamel Zeghida
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  3. Mohammed Redjimi
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Correspondence to Ibrahim Zebiri .

Editor information

Editors and Affiliations

  1. University of Mascara, Mascara, Algeria

    Mohammed Salem

  2. University of Granada, Granada, Spain

    Juan Julián Merelo

  3. Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

  4. University of Mascara, Mascara, Algeria

    Rochdi Bachir Bouiadjra

  5. University of Mascara, Mascara, Algeria

    Mohamed Debakla

  6. University of Mascara, Mascara, Algeria

    Fatima Debbat

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Zebiri, I., Zeghida, D., Redjimi, M. (2023). Enhanced Grey Wolf Optimizer for Data Clustering. In: Salem, M., Merelo, J.J., Siarry, P., Bachir Bouiadjra, R., Debakla, M., Debbat, F. (eds) Artificial Intelligence: Theories and Applications. ICAITA 2022. Communications in Computer and Information Science, vol 1769. Springer, Cham. https://doi.org/10.1007/978-3-031-28540-0_12

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  • DOI: https://doi.org/10.1007/978-3-031-28540-0_12

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