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| Content Provider | Springer Nature Link |
|---|---|
| Author | Wang, Lizhen Han, Jun Chen, Hongmei Lu, Junli |
| Copyright Year | 2016 |
| Abstract | A co-location pattern is a set of spatial features whose instances frequently appear in a spatial neighborhood. This paper efficiently mines the top-k probabilistic prevalent co-locations over spatially uncertain data sets and makes the following contributions: 1) the concept of the top-k probabilistic prevalent co-locations based on a possible world model is defined; 2) a framework for discovering the top-k probabilistic prevalent co-locations is set up; 3) a matrix method is proposed to improve the computation of the prevalence probability of a top-k candidate, and two pruning rules of the matrix block are given to accelerate the search for exact solutions; 4) a polynomial matrix is developed to further speed up the top-k candidate refinement process; 5) an approximate algorithm with compensation factor is introduced so that relatively large quantity of data can be processed quickly. The efficiency of our proposed algorithms as well as the accuracy of the approximation algorithms is evaluated with an extensive set of experiments using both synthetic and real uncertain data sets. |
| Starting Page | 488 |
| Ending Page | 503 |
| Page Count | 16 |
| File Format | |
| ISSN | 20952228 |
| Journal | Frontiers of Computer Science in China |
| Volume Number | 10 |
| Issue Number | 3 |
| e-ISSN | 20952236 |
| Language | English |
| Publisher | Higher Education Press |
| Publisher Date | 2016-03-07 |
| Publisher Institution | Chinese Universities |
| Publisher Place | Beijing |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | spatial co-location mining top-k probabilistic prevalent co-location mining spatially uncertain data sets matrix methods Computer Science |
| Content Type | Text |
| Resource Type | Article |
| Subject | Theoretical Computer Science Computer Science |
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