Abstract
Health risks management such as epidemics study produces large quantity of spatio-temporal data. The development of new methods able to manage such specific characteristics becomes crucial. To tackle this problem, we define a theoretical framework for extracting spatio-temporal patterns (sequences representing evolution of locations and their neighborhoods over time). Classical frequency support doesn’t consider the pattern neighbor neither its evolution over time. We thus propose a new interestingness measure taking into account both spatial and temporal aspects. An algorithm based on pattern-growth approach with efficient successive projections over the database is proposed. Experiments conducted on real datasets highlight the relevance of our method.
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Alatrista Salas, H., Bringay, S., Flouvat, F., Selmaoui-Folcher, N., Teisseire, M. (2012). The Pattern Next Door: Towards Spatio-sequential Pattern Discovery. In: Tan, PN., Chawla, S., Ho, C.K., Bailey, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2012. Lecture Notes in Computer Science(), vol 7302. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30220-6_14
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DOI: https://doi.org/10.1007/978-3-642-30220-6_14
Publisher Name: Springer, Berlin, Heidelberg
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