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    Visual mining of moving flock patterns in large spatio-temporal data sets using a frequent pattern approach

    225761_139067_Int.Journal_of_Geographical_Inf._Sc.pdf (462.8Kb)
    Access Status
    Open access
    Authors
    Turdukulov, Ulanbek
    Romero, A.
    Huisman, O.
    Retsios, V.
    Date
    2014
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Turdukulov, U. and Romero, A. and Huisman, O. and Retsios, V. 2014. Visual mining of moving flock patterns in large spatio-temporal data sets using a frequent pattern approach. International Journal of Geographical Information Science. 28 (10): pp. 2013-2029.
    Source Title
    International Journal of Geographical Information Science
    DOI
    10.1080/13658816.2014.889834
    ISSN
    1365-8816
    School
    Department of Spatial Sciences
    URI
    http://hdl.handle.net/20.500.11937/9332
    Collection
    • Curtin Research Publications
    Abstract

    The popularity of tracking devices continues to contribute to increasing volumes of spatio-temporal data about moving objects. Current approaches in analysing these data are unable to capture collective behaviour and correlations among moving objects. An example of these types of patterns is moving flocks. This article develops an improved algorithm for mining such patterns following a frequent pattern discovery approach, a well-known task in traditional data mining. It uses transaction-based data representation of trajectories to generate a database that facilitates the application of scalable and efficient frequent pattern mining algorithms. Results were compared with an existing method (Basic Flock Evaluation or BFE) and are demonstrated for both synthetic and real data sets with a large number of trajectories. The results illustrate a significant performance increase. Furthermore, the improved algorithm has been embedded into a visual environment that allows manipulation of input parameters and interactive recomputation of the resulting flocks. To illustrate the visual environment a data set containing 30 years of tropical cyclone tracks with 6 hourly observations is used. The example illustrates how the visual environment facilitates exploration and verification of flocks by changing the input parameters and instantly showing the spatio-temporal distribution of the resulting flocks in the Space-Time Cube and interactively selecting,

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