Skip to Main content Skip to Navigation
Conference papers

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.
Document type :
Conference papers
Complete list of metadata

Cited literature [13 references]  Display  Hide  Download
Contributor : Hugo Alatrista-Salas Connect in order to contact the contributor
Submitted on : Tuesday, September 29, 2020 - 2:33:19 PM
Last modification on : Friday, October 22, 2021 - 3:07:32 PM
Long-term archiving on: : Wednesday, December 30, 2020 - 6:46:45 PM


Files produced by the author(s)



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⟩



Record views


Files downloads