Enhancing DBSCAN Accuracy and Computational Efficiency Using Closest Access Point Pre-Clustering for Fingerprint-Based Localization
Within the context of fingerprint database clustering, the density-based spatial clustering of applications with noise (DBSCAN) is notable for its robustness to outliers and ability to handle clusters of different sizes and shapes. However, its high computational burden limits its scalability for de...
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2025-03-01
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| author | Abdulmalik Shehu Yaro Filip Maly Pavel Prazak |
| author_facet | Abdulmalik Shehu Yaro Filip Maly Pavel Prazak |
| author_sort | Abdulmalik Shehu Yaro |
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| description | Within the context of fingerprint database clustering, the density-based spatial clustering of applications with noise (DBSCAN) is notable for its robustness to outliers and ability to handle clusters of different sizes and shapes. However, its high computational burden limits its scalability for dense fingerprint databases. A hybrid two-stage clustering method, the CAP-DBSCAN algorithm, is proposed in this paper, designed to accelerate DBSCAN clustering while ensuring accuracy for fingerprint-based localisation systems. The CAP-DBSCAN algorithm employs the closest access point (CAP) algorithm to pre-cluster the database, while the DBSCAN algorithm performs clustering refinement. It dynamically adjusts the neighborhood radius (Eps) value for each pre-cluster using the k-distance plot method. The performance of the CAP-DBSCAN algorithm is determined across four publicly available received signal strength (RSS)-based fingerprint databases with Euclidean and Manhattan distances as fingerprint similarity metrics. This is benchmarked against the performances of the standard DBSCAN (s-DBSCAN) and k-means++-DBSCAN (k-DBSCAN) algorithms presented in previous research. Simulation results show that the CAP-DBSCAN algorithm consistently outperforms both the s-DBSCAN and k-DBSCAN algorithms, achieving higher silhouette scores, which indicates the generation of more compact and well-defined clusters. Furthermore, the CAP-DBSCAN algorithm demonstrates superior computational efficiency as a result of the CAP algorithm generating well-structured pre-clusters better than those generated by the k-means++ algorithm. This significantly reduces the computational burden of the cluster refinement process. Overall, using Manhattan distance as a fingerprint similarity metric results in the best clustering performance of the CAP-DBSCAN algorithm. These findings underscore the potential of the CAP-DBSCAN algorithm for practical applications in resource-constrained fingerprint-based localization systems.
Doi: 10.28991/HIJ-2025-06-01-022
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| language | English |
| publishDate | 2025-03-01 |
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| spelling | doaj-art-0bd830cb58f14b58b90ba54f3bfcaa832025-08-20T01:51:16ZengItal PublicationHighTech and Innovation Journal2723-95352025-03-016135236210.28991/HIJ-2025-06-01-022258Enhancing DBSCAN Accuracy and Computational Efficiency Using Closest Access Point Pre-Clustering for Fingerprint-Based LocalizationAbdulmalik Shehu Yaro0Filip Maly1Pavel Prazak21) Department of Informatics and Quantitative Methods, Faculty of Informatics and Management, University of Hradec Kralove, 500 03 Hradec Kralove, Czech Republic. 2) Department of Electronics and Telecommunications Engineering, Ahmadu Bello University, Zaria, 810106, Nigeria.Department of Informatics and Quantitative Methods, Faculty of Informatics and Management, University of Hradec Kralove, 500 03 Hradec Kralove,Department of Informatics and Quantitative Methods, Faculty of Informatics and Management, University of Hradec Kralove, 500 03 Hradec Kralove,Within the context of fingerprint database clustering, the density-based spatial clustering of applications with noise (DBSCAN) is notable for its robustness to outliers and ability to handle clusters of different sizes and shapes. However, its high computational burden limits its scalability for dense fingerprint databases. A hybrid two-stage clustering method, the CAP-DBSCAN algorithm, is proposed in this paper, designed to accelerate DBSCAN clustering while ensuring accuracy for fingerprint-based localisation systems. The CAP-DBSCAN algorithm employs the closest access point (CAP) algorithm to pre-cluster the database, while the DBSCAN algorithm performs clustering refinement. It dynamically adjusts the neighborhood radius (Eps) value for each pre-cluster using the k-distance plot method. The performance of the CAP-DBSCAN algorithm is determined across four publicly available received signal strength (RSS)-based fingerprint databases with Euclidean and Manhattan distances as fingerprint similarity metrics. This is benchmarked against the performances of the standard DBSCAN (s-DBSCAN) and k-means++-DBSCAN (k-DBSCAN) algorithms presented in previous research. Simulation results show that the CAP-DBSCAN algorithm consistently outperforms both the s-DBSCAN and k-DBSCAN algorithms, achieving higher silhouette scores, which indicates the generation of more compact and well-defined clusters. Furthermore, the CAP-DBSCAN algorithm demonstrates superior computational efficiency as a result of the CAP algorithm generating well-structured pre-clusters better than those generated by the k-means++ algorithm. This significantly reduces the computational burden of the cluster refinement process. Overall, using Manhattan distance as a fingerprint similarity metric results in the best clustering performance of the CAP-DBSCAN algorithm. These findings underscore the potential of the CAP-DBSCAN algorithm for practical applications in resource-constrained fingerprint-based localization systems. Doi: 10.28991/HIJ-2025-06-01-022 Full Text: PDFhttps://hightechjournal.org/index.php/HIJ/article/view/1183dbscancomputational efficiencyproximity-based clusteringfingerprint-based localizationpre-clustering approach. |
| spellingShingle | Abdulmalik Shehu Yaro Filip Maly Pavel Prazak Enhancing DBSCAN Accuracy and Computational Efficiency Using Closest Access Point Pre-Clustering for Fingerprint-Based Localization HighTech and Innovation Journal dbscan computational efficiency proximity-based clustering fingerprint-based localization pre-clustering approach. |
| title | Enhancing DBSCAN Accuracy and Computational Efficiency Using Closest Access Point Pre-Clustering for Fingerprint-Based Localization |
| title_full | Enhancing DBSCAN Accuracy and Computational Efficiency Using Closest Access Point Pre-Clustering for Fingerprint-Based Localization |
| title_fullStr | Enhancing DBSCAN Accuracy and Computational Efficiency Using Closest Access Point Pre-Clustering for Fingerprint-Based Localization |
| title_full_unstemmed | Enhancing DBSCAN Accuracy and Computational Efficiency Using Closest Access Point Pre-Clustering for Fingerprint-Based Localization |
| title_short | Enhancing DBSCAN Accuracy and Computational Efficiency Using Closest Access Point Pre-Clustering for Fingerprint-Based Localization |
| title_sort | enhancing dbscan accuracy and computational efficiency using closest access point pre clustering for fingerprint based localization |
| topic | dbscan computational efficiency proximity-based clustering fingerprint-based localization pre-clustering approach. |
| url | https://hightechjournal.org/index.php/HIJ/article/view/1183 |
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