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Fully Automated Density-Based Clustering Method

Bilal Bataineh*, Ahmad A. Alzahrani

Information Systems Department, College of Computers and Information Systems, Makkah, Saudi Arabia

* Corresponding Author: Bilal Bataineh. Email: email

Computers, Materials & Continua 2023, 76(2), 1833-1851.


Cluster analysis is a crucial technique in unsupervised machine learning, pattern recognition, and data analysis. However, current clustering algorithms suffer from the need for manual determination of parameter values, low accuracy, and inconsistent performance concerning data size and structure. To address these challenges, a novel clustering algorithm called the fully automated density-based clustering method (FADBC) is proposed. The FADBC method consists of two stages: parameter selection and cluster extraction. In the first stage, a proposed method extracts optimal parameters for the dataset, including the epsilon size and a minimum number of points thresholds. These parameters are then used in a density-based technique to scan each point in the dataset and evaluate neighborhood densities to find clusters. The proposed method was evaluated on different benchmark datasets and metrics, and the experimental results demonstrate its competitive performance without requiring manual inputs. The results show that the FADBC method outperforms well-known clustering methods such as the agglomerative hierarchical method, k-means, spectral clustering, DBSCAN, FCDCSD, Gaussian mixtures, and density-based spatial clustering methods. It can handle any kind of data set well and perform excellently.


Cite This Article

APA Style
Bataineh, B., Alzahrani, A.A. (2023). Fully automated density-based clustering method. Computers, Materials & Continua, 76(2), 1833-1851.
Vancouver Style
Bataineh B, Alzahrani AA. Fully automated density-based clustering method. Comput Mater Contin. 2023;76(2):1833-1851
IEEE Style
B. Bataineh and A.A. Alzahrani, "Fully Automated Density-Based Clustering Method," Comput. Mater. Contin., vol. 76, no. 2, pp. 1833-1851. 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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