Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-30220-6_14 | PAKDD (2) |
Keywords | Field | DocType |
new interestingness measure,spatio-temporal data,large quantity,classical frequency support,pattern next door,pattern neighbor,spatio-sequential pattern discovery,new method,health risks management,efficient successive projection,epidemics study,spatio-temporal pattern | Data mining,Computer science,Artificial intelligence,Machine learning | Conference |
Citations | PageRank | References |
9 | 0.49 | 12 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hugo Alatrista Salas | 1 | 11 | 2.24 |
| Sandra Bringay | 2 | 183 | 34.40 |
| Frédéric Flouvat | 3 | 76 | 16.62 |
| Nazha Selmaoui-Folcher | 4 | 46 | 13.93 |
| Maguelonne Teisseire | 5 | 557 | 129.00 |