Discovering personal gazetteers: An interactive clustering approach

Changqing Zhou, Dan Frankowski, Pamela Ludford, Shashi Shekhar, Loren Terveen

Research output: Contribution to conferencePaperpeer-review

116 Scopus citations

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.

Original languageEnglish (US)
Pages266-273
Number of pages8
StatePublished - Dec 1 2004
EventGIS 2004: Proceedings of the Twelfth ACM International Symposium on Advances in Geographic Information Systems - Washington, DC, United States
Duration: Nov 12 2004Nov 13 2004

Other

OtherGIS 2004: Proceedings of the Twelfth ACM International Symposium on Advances in Geographic Information Systems
CountryUnited States
CityWashington, DC
Period11/12/0411/13/04

Keywords

  • GPS
  • Location-aware
  • Personal gazetteer
  • Personal places
  • Spatiotemporal clustering

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