Name
Abstract | ||
|---|---|---|
Spatial co-location rules represent subsets of spatial features whose instances are frequently located together. This paper studies co-location rule mining on interval data and achieves the following goals: 1) defining the semantic proximity between instances, getting fuzzy equivalent classes of instances and grouping instances in a fuzzy equivalent class into a semantic proximity neighborhood, so that the proximity neighborhood on interval data can be rapidly computed and adjusted; 2) defining new related concepts with co-location rules based on the semantic proximity neighborhood; 3) designing an algorithm to mine the above co-location rules efficiently; 4) verifying the efficiency of the method by experiments on synthetic datasets and the plant dataset of "Three Parallel Rivers of Yunnan Protected Areas". |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-17316-5_45 | ADMA (1) |
Keywords | Field | DocType |
parallel rivers,paper studies co-location rule,fuzzy equivalent class,semantic proximity neighborhood,interval data,co-location rule,semantic proximity,spatial co-location rule,proximity neighborhood,spatial feature,rule based | Data mining,Computer science,Spatial data mining,Fuzzy logic,Rule mining,Artificial intelligence,Machine learning,Interval data | Conference |
Volume | ISSN | ISBN |
6440 | 0302-9743 | 3-642-17315-2 |
Citations | PageRank | References |
6 | 0.48 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lizhen Wang | 1 | 153 | 26.16 |
| Hongmei Chen | 2 | 34 | 5.17 |
| Lihong Zhao | 3 | 6 | 0.48 |
| Lihua Zhou | 4 | 46 | 1.83 |