Name
Abstract | ||
|---|---|---|
In spatial high utility co-location mining, we should consider the utility as a measure of interests by considering the different values of an individual instance that belongs to different feature. This paper focuses on a problem of incremental mining high utility co-locations on spatial databases which are constantly changed with added and disappeared data. Incremental mining high utility co-locations is a complicated process when a spatial database is changed, because added and disappeared data will produce new spatial relationships and take away the existing spatial relationships. The changed relationships can affect the results of high utility co-location mining. So the efficient update of the high utility co-locations is a big challenge for us. This paper presents an efficient algorithm for incremental mining the high utility patterns and evaluates the method by experiments. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/BIGCOMP.2017.7881702 | 2017 IEEE International Conference on Big Data and Smart Computing (BigComp) |
Keywords | Field | DocType |
Spatial data mining,Spatial co-locations,Incremental mining,High utility | Data mining,Algorithm design,Computer science,Spatial database | Conference |
ISSN | ISBN | Citations |
2375-933X | 978-1-5090-3016-3 | 3 |
PageRank | References | Authors |
0.39 | 10 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoxuan Wang | 1 | 17 | 7.52 |
| Lizhen Wang | 2 | 153 | 26.16 |