Name
Abstract | ||
|---|---|---|
Spatial co-location patterns represent the subsets of spatial features which are frequently located together in a geographic space. Spatial co-location pattern mining has been a research hot in recent years. However, maybe the features in a prevalent co-location pattern further have more interesting relationships such as symbiotic relationships, competitive relationships or causal relationships. This paper mines symbiotic relationships implied in prevalent co-location patterns from dynamic spatial databases. Firstly, after analyzing the existed definition of symbiotic patterns, a criterion of judging strong symbiotic patterns is proposed. Secondly, a novel algorithm to mine strong symbiotic patterns from prevalent co-location patterns is presented, named basic algorithm. Third, for improving the efficiency of the basic algorithm, an improved algorithm which integrates two expensive operations of the basic algorithm into together, and a pruning strategy with two pruning lemmas are presented. The experiments evaluate the effectiveness and efficiency of the proposed algorithms with “real + synthetic” data sets and the results show that strong symbiotic patterns are more concise and actionable compared to traditional prevalent co-location patterns. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.knosys.2018.02.006 | Knowledge-Based Systems |
Keywords | Field | DocType |
Spatial data mining,Spatial co-location patterns,Strong symbiotic patterns,Dynamic spatial databases | Data mining,Data set,Computer science,Artificial intelligence,Lemma (mathematics),Machine learning | Journal |
Volume | ISSN | Citations |
146 | 0950-7051 | 4 |
PageRank | References | Authors |
0.40 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junli Lu | 1 | 9 | 1.83 |
| Lizhen Wang | 2 | 153 | 26.16 |
| Yuan Fang | 3 | 16 | 7.74 |
| Jiasong Zhao | 4 | 4 | 1.07 |