## 1 Introduction  
The ship's trajectory refers to the positional information transmitted by a specific system during navigation. This system is called the Automatic Identification System (AIS). The primary function of the AIS, designed for collision avoidance, is an integrated hardware device that uses Very-High-Frequency (VHF) radio waves to broadcast and receive information. It features navigation and assistance functions, providing dynamic information about the vessel. The AIS serves as a crucial communication system for land-based maritime management and acts as a key source of data for the future development of autonomous maritime navigation. Furthermore, the AIS offers advanced functionalities, including maritime traffic flow analysis, warnings for navigational risks such as course deviations and grounding, and applications in maritime incident investigations. It plays a vital role in the ongoing development of the Maritime Intelligent Transportation System (M-ITS). To further maximize the value of AIS data, various algorithms are applied for data mining, enabling rapid and accurate processing of vast amounts of information. More importantly, these algorithms can be integrated with machine learning in the future, allowing for the development of AI models to optimize the processing and analysis of maritime traffic information. The ship's trajectory represents the vessel's navigation path and behavior. Clustering analysis can effectively classify these trajectory groups using algorithms, which helps assess potential risks to navigation safety. The trajectory clustering algorithm proposed in this study, through feature analysis and pattern extraction, yields clustering results that include both grouped and scattered classifications, which can indeed contribute to navigation safety. For example, the scattered data help identify abnormal navigation trajectories, while the clustering results facilitate traffic density calculations. Furthermore, when combined with other maritime information, they can also aid in making optimized routing decisions. Analysis of trajectories produces grouping results that include multiple clusters of trajectories along with scattered noise. The clustered trajectories help analyze traffic density, while the scattered trajectories can explain route deviations or serve as risk warnings. The research results of trajectory clustering offer critical insights for ensuring navigational safety and optimizing maritime decision-making. Consequently, shipping companies and maritime traffic authorities may find this technology of great interest. These suggestions hold significant importance for guiding the future development of the technology. This contributes to the future development of intelligent maritime management technologies. Clustering algorithms are unsupervised methods that can aggregate data into different categories, known as clusters, without prior knowledge. These algorithms are particularly effective for datasets where the number of clusters cannot be predicted in advance. Many clustering algorithms have been proposed in the literature, including K-means, DBSCAN [1], and HDBSCAN [2]. DBSCAN is designed based on the assumption of homogeneous data distribution. In such cases, DBSCAN and HDBSCAN perform similarly. However, for heterogeneous distributions, DBSCAN relies on two parameters, epsilon (_eps_) and minimum sample size (_minPts_), to calculate the clustering thresholds, which require iterative tuning to achieve optimal results. HDBSCAN by contrast, automatically determines _eps_ through density gradients, eliminating the need for manual adjustment. This approach improves clustering performance and noise identification through density stratification. After a thorough comparison, we chose to focus on the application of HDBSCAN, primarily due to its outstanding capability in handling large amounts of noise and big data.