Name
Abstract | ||
|---|---|---|
This paper proposes an algorithm for discovering temporal patterns, represented in the Simple Temporal Problem (STP) formalism, that frequently occur in a set of temporal sequences. To focus the search, some initial knowledge can be provided as a seed pattern by a domain expert: the mining process will find those frequent temporal patterns consistent with the seed. The algorithm has been tested on a database of temporal events obtained from polysomnography tests in patients with Sleep Apnea-Hypopnea Syndrome (SAHS). |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-22218-4_31 | artificial intelligence in medicine in europe |
Keywords | Field | DocType |
mining process,frequent temporal pattern,seed pattern,temporal constraint network,Sleep Apnea-Hypopnea Syndrome,seed knowledge extension,temporal event,initial knowledge,domain expert,Simple Temporal Problem,temporal sequence,temporal pattern | Data mining,Subject-matter expert,Computer science,Artificial intelligence,Formalism (philosophy),Machine learning | Conference |
Citations | PageRank | References |
1 | 0.35 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. R. Álvarez | 1 | 8 | 1.34 |
| patrick felix | 2 | 6 | 0.76 |
| Purificación Cariñena | 3 | 114 | 10.29 |