Name
Abstract | ||
|---|---|---|
Spatial co-location patterns are groups of spatial features whose instances are frequently located together in spatial proximity. Most existing algorithms of discovering spatial co-location patterns are based on the candidate-test model, which is computationally expensive. When the user adjusts the participation index (PI) threshold, these algorithms have to be re-executed from the size 2 co-location patterns. In this paper, we propose a novel spatial instance partition method for mining co-location patterns which called overlap maximal clique partitioning algorithm (OMCP). The OMCP co-location mining algorithm divides instances of an input spatial dataset into a set of overlap maximal cliques. Table instances of all colocation patterns are collected by the overlap maximal cliques. Prevalent co-location patterns are directly calculated without generating the candidate patterns. The OMCP algorithm only needs to execute once to get the PI of all patterns, without re-executing when the PI threshold is adjusted. Our algorithm is performed on both synthetic and real-world datasets to demonstrate that the OMCP algorithm improvements in efficiency of co-location pattern mining. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/MDM.2019.00007 | 2019 20th IEEE International Conference on Mobile Data Management (MDM) |
Keywords | Field | DocType |
spatial data mining, spatial co-location pattern, overlap maximal clique, table instance | Pattern recognition,Clique,Computer science,Artificial intelligence,Data mining algorithm,Partition method,Distributed computing | Conference |
ISSN | ISBN | Citations |
1551-6245 | 978-1-7281-3364-5 | 0 |
PageRank | References | Authors |
0.34 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vanha Tran | 1 | 0 | 2.37 |
| Lizhen Wang | 2 | 153 | 26.16 |
| Lihua Zhou | 3 | 18 | 7.71 |