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

Omer Ajmal (), Shahzad Mumtaz (), Humaira Arshad, Abdullah Soomro, Tariq Hussain, Razaz Waheeb Attar and Ahmed Alhomoud
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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
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

Mathematics, 2024, vol. 12, issue 17, 1-46

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).

Keywords: DENCLUE algorithm; differential evolution; density-based clustering; parameter optimisation; cluster validation metrics; cluster coverage optimisation; unsupervised learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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