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by Changqing Zhou, Dan Frankowski, Pamela Ludford, Shashi Shekhar, Loren Terveen
In Proc. ACMGIS
http://www-users.cs.umn.edu/~dfrankow/files/p098-zhou.pdf
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Abstract:
Personal gazetteers record individuals ’ most important places, such as home, work, grocery store, etc. Using personal gazetteers in location-aware applications offers additional functionality and improves the user experience. However, systems then need some way to acquire them. This paper explores the use of novel semi-automatic techniques to discover gazetteers from users ’ travel patterns (time-stamped location data). There has been previous work on this problem, e.g., using ad hoc algorithms [13] or K-Means clustering [4]; however, both approaches have shortcomings. This paper explores a deterministic, densitybased clustering algorithm that also uses temporal techniques to reduce the number of uninteresting places that are discovered. We introduce a general framework for evaluating personal gazetteer discovery algorithms and use it to demonstrate the advantages of our algorithm over previous approaches.
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