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%0 Journal Article
%4 sid.inpe.br/mtc-m21c/2020/02.12.09.52
%2 sid.inpe.br/mtc-m21c/2020/02.12.09.52.01
%A Monteiro, Diego Vilela,
%A Santos, Rafael Duarte Coelho dos,
%A Ferreira, Karine Reis,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress
%@electronicmailaddress rafael.santos@inpe.br
%@electronicmailaddress karine.ferreira@inpe.br
%T Mining partners in trajectories
%B International Journal of Data Warehousing and Mining
%D 2020
%V 16
%N 1
%8 jan./mar.
%K Data Mining, Moving Objects, Pattern, R, Trajectory.
%X Spatiotemporal data is everywhere, being gathered from different devices such as Earth Observation and GPS satellites, sensor networks and mobile gadgets. Spatiotemporal data collected from moving objects is of particular interest for a broad range of applications. In the last years, such applications have motivated many pieces of research on moving object trajectory data mining. In this article, it is proposed an efficient method to discover partners in moving object trajectories. Such a method identifies pairs of trajectories whose objects stay together during certain periods, based on distance time series analysis. It presents two case studies using the proposed algorithm. This article also describes an R package, called TrajDataMining, that contains algorithms for trajectory data preparation, such as filtering, compressing and clustering, as well as the proposed method Partner.
%P 22-38
%@language en
%9 journal article


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