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Article type: Research Article
Authors: Karli, Sezin | Saygin, Yucel; *
Affiliations: Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey
Correspondence: [*] Corresponding author: Yucel Saygin, Faculty of Engineering and Natural Sciences, Sabanci University, Orhanli, 34956, Tuzla, Istanbul, Turkey. Tel.: +90 216 483 95 76; Fax: +90 216 483 95 50; E-mail: [email protected].
Note: [1] This work was partially funded by the Information Society Technologies Programme of the European Commission, Future and Emerging Technologies under IST-014915 GeoPKDD project.
Abstract: With the advancement of technology, it is now easy to collect the location information of mobile users over time. Spatio-temporal data mining techniques were proposed in the literature for the extraction of patterns from spatio-temporal data. However, current techniques can only extract patterns of the finest time granularity, and therefore overlooks potential patterns available at coarser time granularities. In this work, we propose two techniques to allow mining at different time granularities. Experimental results show that the proposed techniques are indeed effective and efficient for mining periodic spatio-temporal patterns at different time granularities.
Keywords: Data mining, spatio-temporal data, time granularity, periodic pattern
DOI: 10.3233/IDA-2009-0368
Journal: Intelligent Data Analysis, vol. 13, no. 2, pp. 301-335, 2009
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