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However, in real-world scenarios, trajectory outliers can often appear in groups, e.g., a group of bikes that deviates to the usual trajectory due to the maintenance of streets in the context of intelligent transportation. The current paper considers the\n            <jats:italic>Group Trajectory Outlier<\/jats:italic>\n            (GTO) problem and proposes three algorithms. The first and the second algorithms are extensions of the well-known DBSCAN and\n            <jats:italic>k<\/jats:italic>\n            NN algorithms, while the third one models the GTO problem as a feature selection problem. Furthermore, two different enhancements for the proposed algorithms are proposed. The first one is based on ensemble learning and computational intelligence, which allows for merging algorithms\u2019 outputs to possibly improve the final result. The second is a general high-performance computing framework that deals with big trajectory databases, which we used for a GPU-based implementation. Experimental results on different real trajectory databases show the scalability of the proposed approaches.\n          <\/jats:p>","DOI":"10.1145\/3430195","type":"journal-article","created":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T17:22:25Z","timestamp":1609953745000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Machine Learning for Identifying Group Trajectory Outliers"],"prefix":"10.1145","volume":"12","author":[{"given":"Asma","family":"Belhadi","sequence":"first","affiliation":[{"name":"Dept. of Technology, Kristiania University College, Oslo, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youcef","family":"Djenouri","sequence":"additional","affiliation":[{"name":"Dept. of Mathematics and Cybernetics, SINTEF Digital, Oslo, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Djamel","family":"Djenouri","sequence":"additional","affiliation":[{"name":"Computer Science Research Centre, Department of Computer Science 8 Creative Technologies, University of the West of England, Bristol, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomasz","family":"Michalak","sequence":"additional","affiliation":[{"name":"Dept. of Computer Science, Warsaw University, Warsaw, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8768-9709","authenticated-orcid":false,"given":"Jerry Chun-Wei","family":"Lin","sequence":"additional","affiliation":[{"name":"Dept. of Computing, Mathematics, and Physics, Western Norway University of Applied Sciences, Bergen, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,1,6]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2016.2528984"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/335191.335388"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2453975"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2935154"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1080\/00949659608811772"},{"key":"e_1_2_1_6_1","volume-title":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 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