Discovering personal gazetteers
- 12 November 2004
- proceedings article
- Published by Association for Computing Machinery (ACM)
- p. 266-273
- https://doi.org/10.1145/1032222.1032261
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, density-based 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.Keywords
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