Name
Title | ||
|---|---|---|
Analyzing User Trajectories from Mobile Device Data with Hierarchical Dirichlet Processes. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-06483-3_10 | ADVANCES IN ARTIFICIAL INTELLIGENCE, CANADIAN AI 2014 |
Field | DocType | Volume |
Everyday life,Dirichlet process,Activity recognition,Dynamic time warping,Computer science,Mobile device,Artificial intelligence,Application domain,Dirichlet distribution,Machine learning,Trajectory | Conference | 8436 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
8 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Negar Ghourchian | 1 | 1 | 0.69 |
| Doina Precup | 2 | 2829 | 221.83 |