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Online Prediction of People’s Next Point-of-Interest: Concept Drift Support [r-libre/933]

Boukhechba, Mehdi; Bouzouane, Abdenour; Bouchard, Bruno; Gouin-Vallerand, Charles, & Giroux, Sylvain (2015). Online Prediction of People’s Next Point-of-Interest: Concept Drift Support. In Proceedings of the 6th International Workshop on Human Behavior Understanding (HBU 2015, Osaka, Japan, September 8) (p. 97-116). Springer, coll. « Lecture Notes in Computer Science 9277 ». ISBN 978-3-319-24194-4 https://doi.org/10.1007/978-3-319-24195-1_8

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[img]  PDF - Boukhechba_on line_prediction_springer_final.pdf
Content : Accepted Version
 
Item Type: Papers in Conference Proceedings
Refereed: Yes
Status: Published
Abstract: Current advances in location tracking technology provide exceptional amount of data about the users’ movements. The volume of geospatial data collected from moving users’ challenges human ability to analyze the stream of input data. Therefore, new methods for online mining of moving object data are required. One of the popular approaches available for moving objects is the prediction of the unknown future location of an object. In this paper we present a new method for online prediction of users’ next important locations to be visited that not only learns incrementally the users’ habits, but also detects and supports the drifts in their patterns. Our original contribution includes a new algorithm of online mining association rules that support the concept drift.
Official URL: http://link.springer.com/chapter/10.1007%2F978-3-3...
Depositor: Gouin-Vallerand, Charles
Owner / Manager: Charles Gouin-Vallerand
Deposited: 06 May 2016 15:21
Last Modified: 06 May 2016 15:21

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