Name
Abstract | ||
|---|---|---|
There is a variety of interesting knowledge in spatial data sets. Spatial co-location pattern mining can discover sets of different features that are co-located. However, this type of pattern only lists the features that appear together without any consideration of the quantity ratio, which can cause confusion. For example, the co-location pattern {church, restaurants} shows that churches and restaurants are often close to each other, but information such as how many restaurants are near a church is usually not displayed. Also, in real spatial data sets, there is a mutual influence between spatial features, that is, a coupling relationship between different features or the same features. Thus, this paper proposes a novel spatial pattern called a coupling co-location pattern. First, we discuss the properties of the coupling phenomenon between spatial features, and then the concept of coupling co-location patterns is defined formally. Second, the measurement of support and mining framework for coupling co-location patterns are proposed. Finally, we conduct experiments on both real and synthetic data sets, and the results verify the practical significance of coupling co-location patterns. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3233/FAIA200708 | FUZZY SYSTEMS AND DATA MINING VI |
Keywords | DocType | Volume |
Spatial data mining, Coupling co-location pattern (CCP), Maximal clique | Conference | 331 |
ISSN | Citations | PageRank |
0922-6389 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shiran Zhou | 1 | 0 | 0.34 |
| Lizhen Wang | 2 | 0 | 0.68 |
| Pingping Wu | 3 | 0 | 0.34 |