Application of Three-Dimensional Hierarchical Density-Based Spatial Clustering of Applications with Noise in Ship Automatic Identification System Trajectory-Cluster Analysis

Clustering algorithms are widely used in statistical data analysis as a form of unsupervised machine learning, playing a crucial role in big data mining research for Maritime Intelligent Transportation Systems. While numerous studies have explored methods for optimizing ship trajectory clustering, such as narrowing dynamic time windows to prevent errors in time warp calculations or employing the Mahalanobis distance, these methods enhance DBSCAN (Density-Based Spatial Clustering of Applications with Noise) by leveraging trajectory similarity features for clustering. In recent years, machine learning research has rapidly accumulated, and multiple studies have shown that HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) outperforms DBSCAN in achieving accurate and efficient clustering results due to its hierarchical density-based clustering processing technique, particularly in big data mining. This study focuses on the area near Taichung Port in central Taiwan, a crucial maritime shipping route where ship trajectories naturally exhibit a complex and intertwined distribution. Using ship coordinates and heading, the experiment normalized and transformed them into three-dimensional spatial features, employing the HDBSCAN algorithm to obtain optimal clustering results. These results provided a more nuanced analysis compared to human visual observation. This study also utilized O notation and execution time to represent the performance of various methods, with the literature review indicating that HDBSCAN has the same time complexity as DBSCAN but outperforms K-means and other methods. This research involved approximately 293,000 real historical data points and further employed the Silhouette Coefficient and Davies–Bouldin Index to objectively analyze the clustering results. The experiment generated eight clusters with a noise ratio of 12.7%, and the evaluation results consistently demonstrate that HDBSCAN outperforms other methods for big data analysis of ship trajectory clustering.