A Hierarchical Hybrid Closest Access Point–Medoids Algorithm for Improved Clustering-Based Fingerprint Localization
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Traditional clustering algorithms in fingerprint-based localization often struggle with outliers, overlapping clusters, and irregular RSS variations in fingerprint databases, which reduces clustering accuracy. To address these issues, this study proposes a hierarchical hybrid approach, the closest access point–medoids (CAP-medoids) algorithm, which combines the closest access point (CAP) method with k-medoids clustering. The CAP algorithm generates initial clusters based on the strongest received signal strength (RSS) from nearby wireless access points (APs), while k-medoids refines clusters by selecting actual fingerprint vectors as cluster centers, improving robustness against noise and irregular RSS variations. The algorithm was evaluated on four publicly available fingerprint databases of varying size and density. Performance was assessed using Euclidean, Manhattan, and cosine similarity distances as similarity metrics, with silhouette scores and Davies Bouldin (DB) indices as clustering performance metrics. Results show that the CAP-medoids algorithm consistently produces more compact and well-separated clusters than standard k-medoids in small databases, with silhouette scores increasing up to 75% and DB indices decreasing up to 63%. For larger, high-density databases, performance declines, indicating sensitivity to database size. Comparisons with other hybrid algorithms, including CAP+k-means++ and k-density-based spatial clustering of applications with noise (k-DBSCAN) algorithms, confirm its overall robustness and adaptability.
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