Mining following relationships in movement data

Zhenhui Li, Fei Wu, Margaret C. Crofoot

Research output: Contribution to journalConference articlepeer-review

18 Scopus citations


Movement data have been widely collected from GPS and sensors, allowing us to analyze how moving objects interact in terms of space and time and to learn about the relationships that exist among the objects. In this paper, we investigate an interesting relationship that has not been adequately studied so far: the following relationship. Intuitively, a follower has similar trajectories as its leader but always arrives at a location with some time lag. The challenges in mining the following relationship are: (1) the following time lag is usually unknown and varying, (2) the trajectories of the follower and leader are not identical, and (3) the relationship is subtle and only occurs in a short period of time. In this paper, we propose a simple but practical method that addresses all these challenges. It requires only two intuitive parameters and is able to mine following time intervals between two trajectories in linear time. We conduct comprehensive experiments on both synthetic and real datasets to demonstrate the effectiveness of our method.

Original languageEnglish (US)
Article number6729530
Pages (from-to)458-467
Number of pages10
JournalProceedings - IEEE International Conference on Data Mining, ICDM
StatePublished - 2013
Event13th IEEE International Conference on Data Mining, ICDM 2013 - Dallas, TX, United States
Duration: Dec 7 2013Dec 10 2013

All Science Journal Classification (ASJC) codes

  • General Engineering


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