ArticleSensors (Basel, Switzerland)2017
A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Review
- A vessel trajectory similarity measurement and clustering method based on multi-modal convolutional auto-encoder algorithm.Scientific reports · 2026Article
- Review
- Robust Learning with Noisy Ship Trajectories by Adaptive Noise Rate Estimation.Sensors (Basel, Switzerland) · 2023Article
- A Hybrid Rule-Based and Data-Driven Approach to Illegal Transshipment Identification with Interpretable Behavior Features.Sensors (Basel, Switzerland) · 2022Article
- AIS Trajectories Simplification Algorithm Considering Topographic Information.Sensors (Basel, Switzerland) · 2022Article
- Dynamic Maritime Traffic Pattern Recognition with Online Cleaning, Compression, Partition, and Clustering of AIS Data.Sensors (Basel, Switzerland) · 2022Article
- SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction.BMC genomics · 2022Article
- Adaptive Tracking of High-Maneuvering Targets Based on Multi-Feature Fusion Trajectory Clustering: LPI's Purpose.Sensors (Basel, Switzerland) · 2022Article
- 100+ FPS detector of personal protective equipment for worker safety: A deep learning approach for green edge computing.Peer-to-peer networking and applications · 2022Article
- Research on enterprise business model and technology innovation based on artificial intelligence.EURASIP journal on wireless communications and networking · 2021Article
- Water quality assessment and source identification of the Shuangji River (China) using multivariate statistical methods.PloS one · 2021Article
- L-VTP: Long-Term Vessel Trajectory Prediction Based on Multi-Source Data Analysis.Sensors (Basel, Switzerland) · 2019Article
- Ship Spatiotemporal Key Feature Point Online Extraction Based on AIS Multi-Sensor Data Using an Improved Sliding Window Algorithm.Sensors (Basel, Switzerland) · 2019Article
- Partial Discharge Spectral Characterization in HF, VHF and UHF Bands Using Particle Swarm Optimization.Sensors (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top
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