SCALABILITY AND EFFICIENCY OF CLUSTERING ALGORITHMS FOR LARGE-SCALE IoT DATA: A COMPARATIVE ANALYSIS

This research investigates the scalability and efficiency of clustering algorithms applied to large-scale Internet of Things (IoT) data. A comprehensive evaluation is conducted on fourteen clustering algorithms—Affinity Propagation, Agglomerative, BIRCH, Bisecting K-Means, DBSCAN, Fuzzy C-Means, Gaussian Mixtures, HDBSCAN, K-Means, Mean-Shift, OPTICS, Overlapping K-Means, Spectral Clustering, and Ward-Hierarchical—across datasets ranging from 40,000 to 100,000 sensor readings. The study systematically analyzes execution time and clustering performance to determine their suitability for large-scale IoT applications. Results indicate that K-Means, Ward-Hierarchical, and BIRCH exhibit strong scalability and computational efficiency, whereas Affinity Propagation and Spectral Clustering face significant challenges with increasing dataset size. These findings provide valuable guidance for selecting optimal clustering techniques in IoT-based data analytics, considering factors such as computational constraints, dataset characteristics, and clustering granularity.