Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-22351-8_23
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dc.titleFinding closed MEMOs
dc.contributor.authorAung, H.H.
dc.contributor.authorTan, K.-L.
dc.date.accessioned2013-07-04T08:22:49Z
dc.date.available2013-07-04T08:22:49Z
dc.date.issued2011
dc.identifier.citationAung, H.H.,Tan, K.-L. (2011). Finding closed MEMOs. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6809 LNCS : 369-386. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-22351-8_23" target="_blank">https://doi.org/10.1007/978-3-642-22351-8_23</a>
dc.identifier.isbn9783642223501
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/41238
dc.description.abstractCurrent literature lacks a thorough study on the discovery of meeting patterns in moving object datasets. We (a) introduced MEMO, a more precise definition of meeting patterns, (b) proposed three new algorithms based on a novel data-driven approach to extract closed MEMOs from moving object datasets and (c) implemented and evaluated them along with the algorithm previously reported in [6], whose performance has never been evaluated. Experiments using real-world datasets revealed that our filter-and-refinement algorithm outperforms the others in many realistic settings. © 2011 Springer-Verlag Berlin Heidelberg.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-22351-8_23
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1007/978-3-642-22351-8_23
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume6809 LNCS
dc.description.page369-386
dc.identifier.isiutNOT_IN_WOS
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