A clustering-based approach for discovering interesting places in trajectories
Proceedings of the 2008 ACM symposium on Applied computing
Mining significant semantic locations from GPS data
Proceedings of the VLDB Endowment
Discovering areas of interest with geo-tagged images and check-ins
Proceedings of the 20th ACM international conference on Multimedia
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The discovery of a person's personally important places involves obtaining the physical locations for a person's places that matter to his daily life and routines. This problem is driven by the requirements from emerging location-aware applications, which allow a user to pose queries and obtain information in reference to places, e.g., "home", "work" or "Northwest Health Club". It is a challenge to map from physical locations to personally meaningful places because GPS tracks are continuous data both spatially and temporally, while most existing data mining techniques expect discrete data. Previous work has explored algorithms to discover personal places from location data. However, they all have limitations. Our work proposes a two-step approach that discretized continuous GPS data into places and learns important places from the place features. Our approach was validated using real user data and shown to have good accuracy when applied in predicting not only important and frequent places, but also important and not so frequent places.