Topological relations in the world of minimum bounding rectangles: a study with R-trees
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Automatic restoration of polygon models
ACM Transactions on Graphics (TOG)
Information revelation and privacy in online social networks
Proceedings of the 2005 ACM workshop on Privacy in the electronic society
Visual analytics tools for analysis of movement data
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
ACM Transactions on Database Systems (TODS)
Trust-based recommendation systems: an axiomatic approach
Proceedings of the 17th international conference on World Wide Web
Dynamic travel time provision for road networks
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Visually driven analysis of movement data by progressive clustering
Information Visualization
Characterizing user behavior in online social networks
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
T-drive: driving directions based on taxi trajectories
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Adaptive path finding for moving objects
W2GIS'05 Proceedings of the 5th international conference on Web and Wireless Geographical Information Systems
A multi-layer data representation of trajectories in social networks based on points of interest
Proceedings of the twelfth international workshop on Web information and data management
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Sharing of user data has substantially increased over the past few years facilitated by sophisticated Web and mobile applications, including social networks. For instance, users can easily register their trajectories over time based on their daily trips captured with GPS receivers as well as share and relate them with trajectories of other users. Analyzing user trajectories over time can reveal habits and preferences. This information can be used to recommend content to single users or to group users together based on similar trajectories and/or preferences. Recording GPS tracks generates very large amounts of data. Therefore clustering algorithms are required to efficiently analyze such data. In this paper, we focus on investigating ways of efficiently analyzing user trajectories and extracting user preferences from them. We demonstrate an algorithm for clustering user GPS trajectories. In addition, we propose an algorithm to correlate trajectories based on near points between two or more users. The obtained results provided interesting avenues for exploring Location-based Social Network (LBSN) applications.