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The Pattern Next Door: Towards Spatio-sequential Pattern Discovery

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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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00802125
Contributor : Hugo Alatrista-Salas <>
Submitted on : Tuesday, September 29, 2020 - 2:33:19 PM
Last modification on : Wednesday, September 30, 2020 - 3:08:24 AM

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Hugo Alatrista Salas, Sandra Bringay, Frédéric Flouvat, Nazha Selmaoui-Folcher, Maguelonne Teisseire. The Pattern Next Door: Towards Spatio-sequential Pattern Discovery. PAKDD: Pacific-Asia Conference on Knowledge Discovery and Data Mining, May 2012, Kuala Lumpur, Malaysia. pp.157-168, ⟨10.1007/978-3-642-30220-6_14⟩. ⟨lirmm-00802125⟩

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