A Modified Clustering Using Representatives to Enhance and Optimize Tracking and Monitoring of Maritime Traffic in Real-time Using Automatic Identification System Data-Reference-Cited by-同舟云学术

A Modified Clustering Using Representatives to Enhance and Optimize Tracking and Monitoring of Maritime Traffic in Real-time Using Automatic Identification System Data

Published:2021-12 Issue: Volume: Page:
ISSN:
Container-title:2021 International Conference on Computational Science and Computational Intelligence (CSCI)
language:
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Author:

Manyfield-Donald Cheronika¹,Kwembe Tor A.²,Cheng Jing-Ru C.³

Affiliation:

1. Jackson State University,Computational Data-Enabled Science and Engineering,Jackson,MS,USA,39217

2. Jackson State University,Department of Mathematics and Statistical Sciences,Jackson,MS,USA,39217

3. Engineer Research and Development Center U.S. Army Corps of Engineers,Information Technology Lab.,Vicksburg,MS,USA,39180

Funder

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9798893/9798894/09799014.pdf?arnumber=9799014

Reference16 articles.

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2. Clustering Automatic Identification System (AIS) Data using Density-based Spatial Clustering of Large and Complex Datasets on Pairwise Distance Matrix. Springer Nature - Research Book Series: Transactions on Computational Science & Computational Intelligence;manyfield-donald;The 17th International Conference on Data Science (ICDATA'21 July 26-29 2021 USA),2021

3. Modeling Vessel Behaviours by Clustering AIS Data Using Optimized DBSCAN;han;Sustainability,2021

4. Maritime pattern extraction and route reconstruction from incomplete AIS data

5. PCA-based hierarchical clustering of AIS trajectories with automatic extraction of clusters