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Automatic detection of points of interest using spatio-temporal data mining

Basiri, Anahid; Marsh, Stuart; Moore, Terry; Amiran, Pouria


Anahid Basiri

Professor of Geospatial Engineering

Terry Moore

Pouria Amiran


Location Based Services (LBS) are still in their infancy but they are evolving rapidly. It is expected to have more intelligent, adaptive and predictive LBS applications in the future, which can detect users’ intentions and understand their needs, demands and responses. To have such intelligent services, LBS applications should be able to understand users’ behaviours, preferences and interests automatically and without needing users to be asked to specify them. Then, using users’ current situations and previously extracted behaviours, interests and preferences, LBS applications could provide the most appropriate sets of services. This paper shows the application of data mining techniques over anonymous sets of tracking data to recognise mobility behaviours and extract some navigational user preferences such as Point of Interests (PoI) in a format of if-then rules, spatial patterns, models and knowledge. Such knowledge, patterns and models are being used in intelligent navigational services, including navigational decision support applications, smart tourist guides and navigational suggestion making apps.


Basiri, A., Marsh, S., Moore, T., & Amiran, P. (2015). Automatic detection of points of interest using spatio-temporal data mining

Journal Article Type Article
Acceptance Date Mar 17, 2015
Publication Date Sep 15, 2015
Deposit Date Jul 14, 2016
Journal Journal of Mobile Multimedia
Electronic ISSN 1550-4646
Peer Reviewed Peer Reviewed
Volume 11
Issue 3&4
Public URL
Publisher URL
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