Name
Abstract | ||
|---|---|---|
Spatial co-location pattern mining discovers the subsets of features whose events are frequently located together in geographic space. The current research on this topic adopts a threshold-based approach that requires users to specify in advance the thresholds of distance and prevalence. However, in practice, it is not easy to specify suitable thresholds. In this article, we propose a novel iterative mining framework that discovers spatial co-location patterns without predefined thresholds. With the absolute and relative prevalence of spatial co-locations, our method allows users to iteratively select informative edges to construct the neighborhood relationship graph until every significant co-location has enough confidence and eventually to discover all spatial co-location patterns. The experimental results on real world data sets indicate that our framework is effective for prevalent co-locations discovery. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/s10115-012-0506-9 | Knowl. Inf. Syst. |
Keywords | Field | DocType |
Iterative framework, Threshold-free, Spatial co-location pattern, Prevalence reward | Data mining,Graph,Data set,Computer science,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
33 | 2 | 0219-3116 |
Citations | PageRank | References |
5 | 0.41 | 20 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feng Qian | 1 | 56 | 4.26 |
| Qinming He | 2 | 371 | 41.53 |
| Kevin Chiew | 3 | 116 | 11.06 |
| Jiangfeng He | 4 | 42 | 2.59 |