Prediction of next destinations from irregular patterns View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-10

AUTHORS

Mehdi Boukhechba, Abdenour Bouzouane, Sébastien Gaboury, Charles Gouin-Vallerand, Sylvain Giroux, Bruno Bouchard

ABSTRACT

Few decades ago, understanding human behaviors was considered as a mystery where predicting people’s future was impossible. Many changes have been noticed since that era. Thanks to current advances in location tracking technology and data mining techniques, predicting users’ behaviors has become possible. In this paper we present a new algorithm to online predict users’ next visited locations 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 for online association rules mining that supports the concept drift. More... »

PAGES

1345-1357

References to SciGraph publications

  • 2004. Learning with Drift Detection in ADVANCES IN ARTIFICIAL INTELLIGENCE – SBIA 2004
  • 2003. Clustering Mobile Trajectories for Resource Allocation in Mobile Environments in ADVANCES IN INTELLIGENT DATA ANALYSIS V
  • 2002-05-28. Mining Incremental Association Rules with Generalized FP-Tree in ADVANCES IN ARTIFICAL INTELLIGENCE
  • 2006. Prediction of Moving Object Location Based on Frequent Trajectories in COMPUTER AND INFORMATION SCIENCES – ISCIS 2006
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s12652-017-0519-z

    DOI

    http://dx.doi.org/10.1007/s12652-017-0519-z

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1090296719


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