Enhanced Parameter Estimation of DENsity CLUstEring (DENCLUE) Using Differential Evolution
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Published:2024-09-09
Issue:17
Volume:12
Page:2790
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ISSN:2227-7390
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Container-title:Mathematics
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language:en
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Short-container-title:Mathematics
Author:
Ajmal Omer1ORCID, Mumtaz Shahzad12ORCID, Arshad Humaira1ORCID, Soomro Abdullah1ORCID, Hussain Tariq3ORCID, Attar Razaz Waheeb4ORCID, Alhomoud Ahmed5
Affiliation:
1. Department of Computer Science, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan 2. School of Natural and Computing Sciences, University of Aberdeen, Aberdeen AB24 3UE, UK 3. School of Computer Science and Technology, Zhejiang Gongshang University, Hangzhou 310018, China 4. Management Department, College of Business Administration, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia 5. 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).
Funder
Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia Northern Border University, Arar, KSA
Reference66 articles.
1. Gan, G., Ma, C., and Wu, J. (2020). Data Clustering: Theory, Algorithms, and Applications, Society for Industrial and Applied Mathematics. [2nd ed.]. 2. Rodriguez, M.Z., Comin, C.H., Casanova, D., Bruno, O.M., Amancio, D.R., Costa, L.d.F., and Rodrigues, F.A. (2019). Clustering Algorithms: A comparative approach. PLoS ONE, 14. 3. Reddy, C.K., and Vinzamuri, B. (2019). A survey of partitional and hierarchical clustering algorithms. Data Clustering, Chapman and Hall/CRC. 4. Khan, K., Rehman, S.U., Aziz, K., Fong, S., Sarasvady, S., and Vishwa, A. (2014, January 17–19). DBSCAN: Past, present and future. Proceedings of the 5th International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014), Chennai, India. 5. Discovery of arbitrary-shapes clusters using DENCLUE algorithm;Khader;Int. Arab J. Inf. Technol.,2020
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