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.
Type de document :
Communication dans un congrès
PAKDD: Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2012, Kuala Lumpur, Malaysia. Springer, 16th Pacific-Asia Conference, PAKDD 2012, Kuala Lumpur, Malaysia, May 29 – June 1, 2012, Proceedings, Part II, LNCS (7302), pp.157-168, 2012, Advances in Knowledge Discovery and Data Mining, Part II
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00802125
Contributeur : Hugo Alatrista Salas <>
Soumis le : mardi 19 mars 2013 - 10:38:19
Dernière modification le : jeudi 11 janvier 2018 - 06:26:17

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  • HAL Id : lirmm-00802125, version 1

Citation

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, 2012, Kuala Lumpur, Malaysia. Springer, 16th Pacific-Asia Conference, PAKDD 2012, Kuala Lumpur, Malaysia, May 29 – June 1, 2012, Proceedings, Part II, LNCS (7302), pp.157-168, 2012, Advances in Knowledge Discovery and Data Mining, Part II. 〈lirmm-00802125〉

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