Online Prediction of People’s Next Point-of-Interest: Concept Drift Support View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2015

AUTHORS

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

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. More... »

PAGES

97-116

References to SciGraph publications

  • 2007. Mining Frequent Trajectories of Moving Objects for Location Prediction in MACHINE LEARNING AND DATA MINING IN PATTERN RECOGNITION
  • 2004. Learning with Drift Detection in ADVANCES IN ARTIFICIAL INTELLIGENCE – SBIA 2004
  • 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
  • 2003. Clustering Mobile Trajectories for Resource Allocation in Mobile Environments in ADVANCES IN INTELLIGENT DATA ANALYSIS V
  • Book

    TITLE

    Human Behavior Understanding

    ISBN

    978-3-319-24194-4
    978-3-319-24195-1

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-24195-1_8

    DOI

    http://dx.doi.org/10.1007/978-3-319-24195-1_8

    DIMENSIONS

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