Name
Abstract | ||
|---|---|---|
Spatiotemporal event sequences (STESs) are the ordered series of event types whose evolving region-based instances frequently follow each other in time and are located closeby. Previous studies on STES mining require significance and prevalence thresholds for the discovery, which is usually unknown to domain experts. As the quality of the discovered STESs is of great importance to the domain experts who use these algorithms, we introduce a novel class of STES mining algorithms to discover the most relevant STESs without significance and prevalence thresholds. Our algorithms discover the top-K most prevalent spatiotemporal event sequences from R% most significant follow relationships. In the experiments, we conducted a case study using solar event datasets, and compared the performance of the algorithms and the relevance of the discovered sequences. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICDMW.2017.39 | 2017 IEEE International Conference on Data Mining Workshops (ICDMW) |
Keywords | Field | DocType |
Spatiotemporal Knowledge Discovery,Sequence Patterns,Event Sequence Mining | Data mining,Computer science,Artificial intelligence,Event sequence,Semantics,Machine learning,Trajectory | Conference |
ISSN | ISBN | Citations |
2375-9232 | 978-1-5386-3801-9 | 0 |
PageRank | References | Authors |
0.34 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Berkay Aydin | 1 | 40 | 10.75 |
| Ahmet Kucuk | 2 | 3 | 2.42 |
| Soukaina Filali Boubrahimi | 3 | 1 | 6.10 |
| Rafal A. Angryk | 4 | 271 | 45.56 |