Mobile devices have become pervasive among users in both work environments as well as everyday life, and they sense a wealth of information that can be exploited for a variety of tasks, such as activity recognition, security or health monitoring. In this paper, we explore the feasibility of trajectory clustering, i.e., detecting similarities between moving objects, for an application related to workplace productivity improvement. We use Hierarchical Dirichlet Processes due to their ability to automatically extract appropriate trajectory segments. The application domain is the analysis of RSSI data, where this machine learning method proves successfully. © 2014 Springer International Publishing.
CITATION STYLE
Ghourchian, N., & Precup, D. (2014). Analyzing user trajectories from mobile device data with hierarchical Dirichlet processes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8436 LNAI, pp. 107–118). Springer Verlag. https://doi.org/10.1007/978-3-319-06483-3_10
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