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Mining Partners in Trajectories

Mining Partners in Trajectories

Diego Vilela Monteiro, Rafael Duarte Coelho dos Santos, Karine Reis Ferreira
Copyright: © 2020 |Volume: 16 |Issue: 1 |Pages: 17
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781799804970|DOI: 10.4018/IJDWM.2020010102
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MLA

Monteiro, Diego Vilela, et al. "Mining Partners in Trajectories." IJDWM vol.16, no.1 2020: pp.22-38. http://doi.org/10.4018/IJDWM.2020010102

APA

Monteiro, D. V., Coelho dos Santos, R. D., & Ferreira, K. R. (2020). Mining Partners in Trajectories. International Journal of Data Warehousing and Mining (IJDWM), 16(1), 22-38. http://doi.org/10.4018/IJDWM.2020010102

Chicago

Monteiro, Diego Vilela, Rafael Duarte Coelho dos Santos, and Karine Reis Ferreira. "Mining Partners in Trajectories," International Journal of Data Warehousing and Mining (IJDWM) 16, no.1: 22-38. http://doi.org/10.4018/IJDWM.2020010102

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Abstract

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.

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