## 1 Introduction
### Background and Purpose
To address these issues, they used a convolutional autoencoder to convert ship trajectories into 2D trajectory images for similarity calculations. Finding route patterns or route clustering using similarity measures between ship trajectories is limited in application to large datasets or long routes [15]. It may produce poor clustering results owing to the sensitivity to noise and outliers, necessitating effective preprocessing and noise reduction techniques [16]. Furthermore, various results can be obtained owing to different methods and parameter selections, making it difficult to achieve consistency and complicating the validation and comparison of clustering results [17]. In recent studies, [PERSON] et al. [18] and [PERSON] and [PERSON] [19] aimed to improve the accuracy and efficiency of ship trajectory clustering by modifying and enhancing the Hausdorff distance method. [PERSON] et al. and [PERSON] and [PERSON] proposed a technique that measures static distances based on the dynamic characteristics of ships, such as ship speed and direction, to calculate the similarity of ship trajectories more accurately. Although the accuracy of ship trajectory similarity was enhanced, the algorithm's complexity introduced limitations in real-time applications and highlighted its sensitivity to data quality. [PERSON] et al. [20] introduced a novel ship trajectory clustering method to identify similar ship movement patterns. They proposed a method that converts ship trajectories into behavioral sequences using hierarchical representations, improving ship trajectory clustering by employing predictive clustering to account for how ship movements change over time rather than treating ship trajectories as static sequences. However, the understanding of its relative performance remains limited due to the lack of detailed information regarding computational resources. To address these issues, we aimed to develop an algorithm that can produce consistent cluster analysis results while proposing a preprocessing method that uses solely ship location data, applies to large datasets, and effectively reduces noise. We selected the DBSCAN algorithm because it can handle large-scale data, identify noise clusters, and provide consistent clustering results. We propose a method for automatically setting the parameters of the DBSCAN algorithm. Additionally, we re-analyzed the results and termination criteria using the route clustering algorithm to distinguish sub-routes from the main routes. The main components of the proposed route clustering algorithm are as follows:  
Step 1: Data Preprocessing  
Organize extracted and segmented ship trajectories to induce minimal deformation of the trajectories. Combined dimension reduction techniques were used to transform the data for statistical trajectory distance measurement and cluster analysis to verify ship trajectory similarity. Step 2: Clustering using the DBSCAN Algorithm  
We selected the DBSCAN algorithm, which can analyze large amounts of data, provide consistent analysis results, and explore noise clusters to cluster preprocessed data. Propose and add to the existing algorithm a method to automatically optimize DBSCAN parameters. Epsilon and MinPts. Step 3: Sub-Route Clustering  
Apply a sub-route clustering process to the algorithm to further segment ship routes. Route clustering was performed by presenting the criteria for cluster reanalysis based on inter-data distances and data density.