Name
Abstract | ||
|---|---|---|
Co-location pattern mining refers to the task of discovering the group of features (geographic object types) whose instances (geographic objects) are frequently located close together in a geometric space. Current approaches on this topic adopt a prevalence threshold (a measure of a user's interest in a pattern) to generate prevalent co-location patterns. However, in practice, it is not easy to specify a suitable prevalence threshold. Thus, users have to repeatedly execute the program to find a suitable prevalence threshold. Besides, the efficiency of these approaches is limited because of the expensive cost of identifying row-instances of co-location patterns. In this paper, we propose a novel clique-based approach for discovering complete and correct prevalent co-location patterns. The proposed approach avoids identifying row-instances of co-location patterns thus making it much easier to find a proper prevalence threshold. First, two efficient schemas are designed to generate complete and correct cliques. Next, these cliques are transformed into a hash structure which is independent of the prevalence threshold. Finally, the prevalence of each co-location pattern is efficiently calculated using the hash structure. The experiments on both real and synthetic datasets show the efficiency and effectiveness of our proposed approaches. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.ins.2019.03.072 | Information Sciences |
Keywords | Field | DocType |
Spatial data mining,Co-location pattern,Spatial neighbor relationship,Row-instance,Clique-based approach | Clique,Object type,Theoretical computer science,Hash function,Artificial intelligence,Schema (psychology),Mathematics,Machine learning | Journal |
Volume | ISSN | Citations |
490 | 0020-0255 | 4 |
PageRank | References | Authors |
0.39 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuguang Bao | 1 | 39 | 4.75 |
| Lizhen Wang | 2 | 153 | 26.16 |