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Enhanced Parameter Estimation of DENsity CLUstEring (DENCLUE) Using Differential Evolution

Author

Listed:
  • Omer Ajmal

    (Department of Computer Science, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan)

  • Shahzad Mumtaz

    (Department of Computer Science, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
    School of Natural and Computing Sciences, University of Aberdeen, Aberdeen AB24 3UE, UK)

  • Humaira Arshad

    (Department of Computer Science, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan)

  • Abdullah Soomro

    (Department of Computer Science, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan)

  • Tariq Hussain

    (School of Computer Science and Technology, Zhejiang Gongshang University, Hangzhou 310018, China)

  • Razaz Waheeb Attar

    (Management Department, College of Business Administration, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Ahmed Alhomoud

    (Department of Computer Science, Faculty of Science, Northern Border University, Arar 91431, Saudi Arabia)

Abstract

The task of finding natural groupings within a dataset exploiting proximity of samples is known as clustering, an unsupervised learning approach. Density-based clustering algorithms, which identify arbitrarily shaped clusters using spatial dimensions and neighbourhood aspects, are sensitive to the selection of parameters. For instance, DENsity CLUstEring (DENCLUE)—a density-based clustering algorithm—requires a trial-and-error approach to find suitable parameters for optimal clusters. Earlier attempts to automate the parameter estimation of DENCLUE have been highly dependent either on the choice of prior data distribution (which could vary across datasets) or by fixing one parameter (which might not be optimal) and learning other parameters. This article addresses this challenge by learning the parameters of DENCLUE through the differential evolution optimisation technique without prior data distribution assumptions. Experimental evaluation of the proposed approach demonstrated consistent performance across datasets (synthetic and real datasets) containing clusters of arbitrary shapes. The clustering performance was evaluated using clustering validation metrics (e.g., Silhouette Score, Davies–Bouldin Index and Adjusted Rand Index) as well as qualitative visual analysis when compared with other density-based clustering algorithms, such as DPC, which is based on weighted local density sequences and nearest neighbour assignments (DPCSA) and Variable KDE-based DENCLUE (VDENCLUE).

Suggested Citation

  • Omer Ajmal & Shahzad Mumtaz & Humaira Arshad & Abdullah Soomro & Tariq Hussain & Razaz Waheeb Attar & Ahmed Alhomoud, 2024. "Enhanced Parameter Estimation of DENsity CLUstEring (DENCLUE) Using Differential Evolution," Mathematics, MDPI, vol. 12(17), pages 1-46, September.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:17:p:2790-:d:1474514
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    References listed on IDEAS

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    1. Yordan P Raykov & Alexis Boukouvalas & Fahd Baig & Max A Little, 2016. "What to Do When K-Means Clustering Fails: A Simple yet Principled Alternative Algorithm," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-28, September.
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    Cited by:

    1. Omer Ajmal & Humaira Arshad & Muhammad Asad Arshed & Saeed Ahmed & Shahzad Mumtaz, 2024. "Robust Parameter Optimisation of Noise-Tolerant Clustering for DENCLUE Using Differential Evolution," Mathematics, MDPI, vol. 12(21), pages 1-38, October.

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