Name
Abstract | ||
|---|---|---|
There is a growing interest in leveraging geo-spatial data to provide location-aware services. With a large amount of collected geo-spatial data, a crucial step is to identify important "base" locations (e.g., home or work) and understand users' behavior at these locations. In this paper, we propose an unsupervised collaborative learning approach to identifying home and work locations of individuals from geo-spatial trajectory data. Our approach transforms user trajectory records into intuitive and insightful user-location signatures, clusters these signatures, and then identifies location types based on cluster characteristics. This clustering model can be used to identify base locations for new users. We validate this approach using Open Street Map and Foursquare location tags and obtain an accuracy of 80%. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/MDM.2016.53 | 2016 17th IEEE International Conference on Mobile Data Management (MDM) |
Keywords | Field | DocType |
spatio-temporal analysis,user mobility behavior | Work Locations,Data mining,Collaborative learning,Spatio-Temporal Analysis,Computer science,Open street map,Cluster analysis,Trajectory | Conference |
Volume | ISBN | Citations |
1 | 978-1-5090-0884-1 | 1 |
PageRank | References | Authors |
0.35 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rong Liu | 1 | 62 | 4.56 |
| Swapna | 2 | 45 | 4.94 |
| Wesley M. Gifford | 3 | 2 | 1.40 |
| Anshul Sheopuri | 4 | 51 | 7.85 |