Development of HTC-DBSCAN: A Hierarchical Trajectory Clustering Algorithm with Automated Parameter Tuning
Existing route-clustering methods often fail to identify abnormal sections or similarities between routes, mainly when working with large or long datasets. While sub-route clustering can detect regional patterns, it struggles to accurately capture the overall route structure. The present study propo...
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MDPI AG
2024-11-01
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| author | Dae-Han Lee Joo-Sung Kim |
| author_facet | Dae-Han Lee Joo-Sung Kim |
| author_sort | Dae-Han Lee |
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| description | Existing route-clustering methods often fail to identify abnormal sections or similarities between routes, mainly when working with large or long datasets. While sub-route clustering can detect regional patterns, it struggles to accurately capture the overall route structure. The present study proposes a new ship route-clustering method that enhances computational efficiency and noise recognition while addressing these limitations. We refined Automatic Identification System data via four data-cleaning processes and applied a statistical distance measurement to assess ship trajectory similarity. Dimensionality reduction was then used to facilitate clustering. The clustering of ship route similarities is non-parametric and can be applied to datasets not separated based on density to find clusters of various densities. Density-Based Spatial Clustering of Applications (DBSCA) applies to many research fields; using the DBSCA with Noise (DBSCAN) algorithm, we propose an improved DBSCAN algorithm that automatically determines the parameters Epsilon and MinPts. In this study, as a core ship route-clustering process, we propose a sub-route clustering process by setting the distance and density of data points to clear standards for re-analysis and completion. The proposed approach demonstrates markedly enhanced clustering performance, offering a more sophisticated and efficient basis for ship route decision-making. |
| format | Article |
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| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
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| spelling | doaj-art-bb6818990cc54e0aabb66e7d585dbbd52025-08-20T01:55:41ZengMDPI AGApplied Sciences2076-34172024-11-0114231099510.3390/app142310995Development of HTC-DBSCAN: A Hierarchical Trajectory Clustering Algorithm with Automated Parameter TuningDae-Han Lee0Joo-Sung Kim1Graduate School of Maritime Transportation System, Mokpo National Maritime University, Mokpo 58628, Republic of KoreaDivision of Navigation Science, Mokpo National Maritime University, Mokpo 58628, Republic of KoreaExisting route-clustering methods often fail to identify abnormal sections or similarities between routes, mainly when working with large or long datasets. While sub-route clustering can detect regional patterns, it struggles to accurately capture the overall route structure. The present study proposes a new ship route-clustering method that enhances computational efficiency and noise recognition while addressing these limitations. We refined Automatic Identification System data via four data-cleaning processes and applied a statistical distance measurement to assess ship trajectory similarity. Dimensionality reduction was then used to facilitate clustering. The clustering of ship route similarities is non-parametric and can be applied to datasets not separated based on density to find clusters of various densities. Density-Based Spatial Clustering of Applications (DBSCA) applies to many research fields; using the DBSCA with Noise (DBSCAN) algorithm, we propose an improved DBSCAN algorithm that automatically determines the parameters Epsilon and MinPts. In this study, as a core ship route-clustering process, we propose a sub-route clustering process by setting the distance and density of data points to clear standards for re-analysis and completion. The proposed approach demonstrates markedly enhanced clustering performance, offering a more sophisticated and efficient basis for ship route decision-making.https://www.mdpi.com/2076-3417/14/23/10995ship route clusteringship trajectory similaritysub-route clusteringDBSCANautomated parameter tuningMASS technology |
| spellingShingle | Dae-Han Lee Joo-Sung Kim Development of HTC-DBSCAN: A Hierarchical Trajectory Clustering Algorithm with Automated Parameter Tuning Applied Sciences ship route clustering ship trajectory similarity sub-route clustering DBSCAN automated parameter tuning MASS technology |
| title | Development of HTC-DBSCAN: A Hierarchical Trajectory Clustering Algorithm with Automated Parameter Tuning |
| title_full | Development of HTC-DBSCAN: A Hierarchical Trajectory Clustering Algorithm with Automated Parameter Tuning |
| title_fullStr | Development of HTC-DBSCAN: A Hierarchical Trajectory Clustering Algorithm with Automated Parameter Tuning |
| title_full_unstemmed | Development of HTC-DBSCAN: A Hierarchical Trajectory Clustering Algorithm with Automated Parameter Tuning |
| title_short | Development of HTC-DBSCAN: A Hierarchical Trajectory Clustering Algorithm with Automated Parameter Tuning |
| title_sort | development of htc dbscan a hierarchical trajectory clustering algorithm with automated parameter tuning |
| topic | ship route clustering ship trajectory similarity sub-route clustering DBSCAN automated parameter tuning MASS technology |
| url | https://www.mdpi.com/2076-3417/14/23/10995 |
| work_keys_str_mv | AT daehanlee developmentofhtcdbscanahierarchicaltrajectoryclusteringalgorithmwithautomatedparametertuning AT joosungkim developmentofhtcdbscanahierarchicaltrajectoryclusteringalgorithmwithautomatedparametertuning |