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dc.contributor.advisorWhitaker, Lyn R.
dc.contributor.authorHintze, John R.
dc.dateSep-17
dc.date.accessioned2017-11-07T23:39:33Z
dc.date.available2017-11-07T23:39:33Z
dc.date.issued2017-09
dc.identifier.urihttp://hdl.handle.net/10945/56135
dc.description.abstractIn this thesis, we cluster stop points into stop-point regions using one month’s Automatic Identification System (AIS) data from the Gulf of Mexico and Caribbean Sea to characterize vessel behavior in an area with diverse traffic patterns. Initial cleaning of the dataset is necessary to address multiple issues common to AIS transponders. We consider methods for computing inter-point distances. In particular, we study a promising method for combining geospatial coordinates with other vessel attributes. We use the Ordering Points To Identify the Cluster Structure (OPTICS) clustering algorithm because it can identify outliers, and it constructs clusters of varying shapes and densities. Our best results come from dividing the area of interest into seven zones of equal size, and analyzing the results over each zone. Using classification trees to develop a classification tool, we illustrate an approach for predicting the cluster membership of a new observation. Due to the reduction in computation time and accuracy of results, we recommend that further research utilize the methods from this study as the foundation for an automated threat detection system.en_US
dc.description.urihttp://archive.org/details/annalysisofvesse1094556135
dc.publisherMonterey, California: Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleAn analysis of vessel waypoint behavior through data clusteringen_US
dc.typeThesisen_US
dc.contributor.secondreaderKoyak, Robert A.
dc.contributor.departmentOperations Research (OR)
dc.subject.authordata analysisen_US
dc.subject.authorautomated identification systemen_US
dc.subject.authorclusteringen_US
dc.subject.authoranomaly detectionen_US
dc.description.serviceEnsign, United States Navyen_US
etd.thesisdegree.nameMaster of Science in Applied Science (Operations Research)en_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineApplied Science (Operations Research)en_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.


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