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Solving text clustering problem using a memetic differential evolution algorithm

Author

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  • Hossam M J Mustafa
  • Masri Ayob
  • Dheeb Albashish
  • Sawsan Abu-Taleb

Abstract

The text clustering is considered as one of the most effective text document analysis methods, which is applied to cluster documents as a consequence of the expanded big data and online information. Based on the review of the related work of the text clustering algorithms, these algorithms achieved reasonable clustering results for some datasets, while they failed on a wide variety of benchmark datasets. Furthermore, the performance of these algorithms was not robust due to the inefficient balance between the exploitation and exploration capabilities of the clustering algorithm. Accordingly, this research proposes a Memetic Differential Evolution algorithm (MDETC) to solve the text clustering problem, which aims to address the effect of the hybridization between the differential evolution (DE) mutation strategy with the memetic algorithm (MA). This hybridization intends to enhance the quality of text clustering and improve the exploitation and exploration capabilities of the algorithm. Our experimental results based on six standard text clustering benchmark datasets (i.e. the Laboratory of Computational Intelligence (LABIC)) have shown that the MDETC algorithm outperformed other compared clustering algorithms based on AUC metric, F-measure, and the statistical analysis. Furthermore, the MDETC is compared with the state of art text clustering algorithms and obtained almost the best results for the standard benchmark datasets.

Suggested Citation

  • Hossam M J Mustafa & Masri Ayob & Dheeb Albashish & Sawsan Abu-Taleb, 2020. "Solving text clustering problem using a memetic differential evolution algorithm," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-18, June.
  • Handle: RePEc:plo:pone00:0232816
    DOI: 10.1371/journal.pone.0232816
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    References listed on IDEAS

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    1. Mayra Z Rodriguez & Cesar H Comin & Dalcimar Casanova & Odemir M Bruno & Diego R Amancio & Luciano da F Costa & Francisco A Rodrigues, 2019. "Clustering algorithms: A comparative approach," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-34, January.
    2. Hossam M J Mustafa & Masri Ayob & Mohd Zakree Ahmad Nazri & Graham Kendall, 2019. "An improved adaptive memetic differential evolution optimization algorithms for data clustering problems," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-28, May.
    3. Rahab M Ramadan & Safa M Gasser & Mohamed S El-Mahallawy & Karim Hammad & Ahmed M El Bakly, 2018. "A memetic optimization algorithm for multi-constrained multicast routing in ad hoc networks," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-17, March.
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    Cited by:

    1. Amiri, Babak & Karimianghadim, Ramin, 2024. "A novel text clustering model based on topic modelling and social network analysis," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).

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