3/2021 - 9 |
A Novel Approach for Knowledge Discovery from AIS Data: An Application for Transit Marine Traffic in the Sea of MarmaraDOGAN, Y. , KART, O. , KUNDAKCI, B. , NAS, S. |
Extra paper information in |
Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science |
Download PDF (3,179 KB) | Citation | Downloads: 904 | Views: 1,062 |
Author keywords
clustering algorithms, genetic algorithms, knowledge discovery, machine learning, radar signal processing.
References keywords
data(28), maritime(12), traffic(6), icbda(6), ship(5), ocean(5), automatic(5), mining(4), marine(4), identification(4)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2021-08-31
Volume 21, Issue 3, Year 2021, On page(s): 73 - 80
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2021.03009
Web of Science Accession Number: 000691632000009
SCOPUS ID: 85114809235
Abstract
This paper addresses the discovery of hidden patterns in the data of Automatic Identification Systems by a novel clustering model using data processing and data mining methods. It reveals the transit tracks and the transit vessels on these tracks in the Sea of Marmara which has a dense marine traffic. In this study, improved Density Based Spatial Clustering of Applications with Noise and KMeans++ clustering algorithms have been used together with complex database queries. This proposed approach has been compared to other clustering algorithms such as Self-Organizing Map, Hierarchical Clustering with Single-Link and Genetic Clustering. It has been observed that these alternative algorithms could not reach high accuracy values and they could not give the expected tracks. The proposed approach has five steps and experimental results demonstrate that when this novel approach has been applied step by step, the results can match the observed data by The Republic of Turkey, Ministry of Transport, Maritime and Communications by 95%. Finally, this novel approach is suggested to maritime authorities for all the seas in the world to manage the vessel traffic which has big and complex data. |
References | | | Cited By |
Web of Science® Times Cited: 2 [View]
View record in Web of Science® [View]
View Related Records® [View]
Updated 2 days, 14 hours ago
SCOPUS® Times Cited: 3
View record in SCOPUS® [Free preview]
View citations in SCOPUS® [Free preview]
[1] Research on Vessel Speed Heading and Collision Detection Method Based on AIS Data, Wang, Guoqing, Fan, En, Zheng, Guohua, Li, Kexiang, Huang, Haiguang, Tang, Yajuan, Mobile Information Systems, ISSN 1875-905X, Issue , 2022.
Digital Object Identifier: 10.1155/2022/7257075 [CrossRef]
[2] Scalable framework for AIS data exploration through effective density visualizations, Troupiotis-Kapeliaris, Alexandros, Tsili, Eleni, Kaliorakis, Manolis, Spiliopoulos, Giannis, Zissis, Dimitris, OCEANS 2023 - Limerick, ISBN 979-8-3503-3226-1, 2023.
Digital Object Identifier: 10.1109/OCEANSLimerick52467.2023.10244698 [CrossRef]
Disclaimer: All information displayed above was retrieved by using remote connections to respective databases. For the best user experience, we update all data by using background processes, and use caches in order to reduce the load on the servers we retrieve the information from. As we have no control on the availability of the database servers and sometimes the Internet connectivity may be affected, we do not guarantee the information is correct or complete. For the most accurate data, please always consult the database sites directly. Some external links require authentication or an institutional subscription.
Web of Science® is a registered trademark of Clarivate Analytics, Scopus® is a registered trademark of Elsevier B.V., other product names, company names, brand names, trademarks and logos are the property of their respective owners.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.