Show simple item record

dc.contributor.authorTurdukulov, Ulanbek
dc.contributor.authorRomero, A.
dc.contributor.authorHuisman, O.
dc.contributor.authorRetsios, V.
dc.date.accessioned2017-01-30T11:11:56Z
dc.date.available2017-01-30T11:11:56Z
dc.date.created2015-04-28T20:00:43Z
dc.date.issued2014
dc.identifier.citationTurdukulov, 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.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/9332
dc.identifier.doi10.1080/13658816.2014.889834
dc.description.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,

dc.publisherTaylor and Francis
dc.subjectSpace-Time Cube
dc.subjectvisual mining
dc.subjecttropical cyclones
dc.subjectspatio-temporal data sets
dc.subjectflock patterns
dc.subjectfrequent pattern mining
dc.titleVisual mining of moving flock patterns in large spatio-temporal data sets using a frequent pattern approach
dc.typeJournal Article
dcterms.source.volume28
dcterms.source.number10
dcterms.source.startPage2013
dcterms.source.endPage2029
dcterms.source.issn1365-8816
dcterms.source.titleInternational Journal of Geographical Information Science
curtin.departmentDepartment of Spatial Sciences
curtin.accessStatusOpen access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record