Name
Title | ||
|---|---|---|
Efficient Discovery Of Co-Location Patterns From Massive Spatial Datasets With Or Without Rare Features |
Abstract | ||
|---|---|---|
A co-location pattern indicates a group of spatial features whose instances are frequently located together in proximate geographic area. Spatial co-location pattern mining (SCPM) is valuable for many practical applications. Numerous previous SCPM studies emphasize the equal participation per feature. As a result, the interesting co-locations with rare features cannot be captured. In this paper, we propose a novel interest measure, i.e., the weighted participation index (WPI), to identify co-locations with or without rare features. The WPI measure possesses a conditional anti-monotone property which can be utilized to prune the search space. In addition, a fast row instance identification mechanism based on the ordered NR-tree is proposed to enhance efficiency. Subsequently, the ordered NR-tree-based algorithm is developed. To further improve efficiency and process massive spatial data, we break the ordered NR-tree into multiple independent subtrees, and parallelize the ordered NR-tree-based algorithm on MapReduce framework. Extensive experiments are conducted on both real and synthetic datasets to verify the effectiveness, efficiency and scalability of our techniques. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s10115-021-01559-3 | KNOWLEDGE AND INFORMATION SYSTEMS |
Keywords | DocType | Volume |
Spatial data mining, Co-location pattern, Rare feature, Parallel algorithm | Journal | 63 |
Issue | ISSN | Citations |
6 | 0219-1377 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peizhong Yang | 1 | 22 | 6.85 |
| Lizhen Wang | 2 | 153 | 26.16 |
| Xiaoxuan Wang | 3 | 17 | 7.52 |
| Lihua Zhou | 4 | 0 | 0.34 |