IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i21p3367-d1507836.html
   My bibliography  Save this article

Robust Parameter Optimisation of Noise-Tolerant Clustering for DENCLUE Using Differential Evolution

Author

Listed:
  • Omer Ajmal

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

  • Humaira Arshad

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

  • Muhammad Asad Arshed

    (School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan)

  • Saeed Ahmed

    (School of Systems and Technology, University of Management and Technology, Lahore 54770, Pakistan
    Department of Experimental Medical Science, Biomedical Center (BMC), Lund University, 22184 Lund, Sweden)

  • 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)

Abstract

Clustering samples based on similarity remains a significant challenge, especially when the goal is to accurately capture the underlying data clusters of complex arbitrary shapes. Existing density-based clustering techniques are known to be best suited for capturing arbitrarily shaped clusters. However, a key limitation of these methods is the difficulty in automatically finding the optimal set of parameters adapted to dataset characteristics, which becomes even more challenging when the data contain inherent noise. In our recent work, we proposed a Differential Evolution-based DENsity CLUstEring (DE-DENCLUE) to optimise DENCLUE parameters. This study evaluates DE-DENCLUE for its robustness in finding accurate clusters in the presence of noise in the data. DE-DENCLUE performance is compared against three other density-based clustering algorithms—DPC based on weighted local density sequence and nearest neighbour assignment (DPCSA), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Variable Kernel Density Estimation–based DENCLUE (VDENCLUE)—across several datasets (i.e., synthetic and real). The study has consistently shown superior results for DE-DENCLUE compared to other models for most datasets with different noise levels. Clustering quality metrics such as the Silhouette Index (SI), Davies–Bouldin Index (DBI), Adjusted Rand Index (ARI), and Adjusted Mutual Information (AMI) consistently show superior SI, ARI, and AMI values across most datasets at different noise levels. However, in some cases regarding DBI, the DPCSA performed better. In conclusion, the proposed method offers a reliable and noise-resilient clustering solution for complex datasets.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:21:p:3367-:d:1507836
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/21/3367/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/21/3367/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:21:p:3367-:d:1507836. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.