Discovering personal gazetteers: an interactive clustering approach

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. Categories and Subject Descriptors H.3.3 [Information Search and Retri...

• Type: Conference
• Year: 2004
• Where: GIS
• Authors: Changqing Zhou, Dan Frankowski, Pamela J. Ludford, Shashi Shekhar, Loren G. Terveen
• Keywords: Database, Densitybased Clustering Algorithm, Gazetteers Record Individuals, GIS 2004, Personal Gazetteers