Name
Title | ||
|---|---|---|
Time-efficient significance measure for discovering spatiotemporal co-occurrences from data with unbalanced characteristics. |
Abstract | ||
|---|---|---|
Mining spatiotemporal co-occurrence patterns requires assessing the strength of co-occurrences among the instances of different feature types. Currently, a spatiotemporal version of the Jaccard measure is used for measuring the strength of spatiotemporal co-occurrences. We present an extended spatiotemporal version of the Jaccard measure (J*) that is more relevant and efficient for the task of STCOP mining. We also demonstrate the space and time efficiency of the J* with experimental evaluation. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2820783.2820871 | SIGSPATIAL/GIS |
Keywords | Field | DocType |
Spatiotemporal co-occurrence pattern, interestingness measure, spatiotemporal knowledge discovery | Data mining,Computer science,Jaccard index,Artificial intelligence,Machine learning | Conference |
Citations | PageRank | References |
6 | 0.49 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Berkay Aydin | 1 | 40 | 10.75 |
| vijay akkineni | 2 | 10 | 1.73 |
| Rafal A. Angryk | 3 | 271 | 45.56 |