Name
Title | ||
|---|---|---|
Case-based reasoning approach in geographical data mining: Experiement and application |
Abstract | ||
|---|---|---|
The study deems the CBR approach as a kind of problem-oriented spatial data mining method and provides case-based similarity and reasoning algorithms to extract knowledge from geographical data. First, this paper provides problem-oriented method to represent and organize geographical cases. Second, a rough set theory-based approach was employed to quantitatively retrieve these inherent spatial relationships. Third, a general model was then proposed to calculate the spatial similarity among geographic cases considering different spatial characteristics and relationships of geographical cases. The CBR method was then tested by studying a typical geographic phenomenon, Results of the studies show that CBR method has its advantages in quantitatively analyzing spatial data as well as in solving geographical problems. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/IGARSS.2009.5417685 | IGARSS |
Keywords | Field | DocType |
rough set theory,similarity algorithm,case representation,case-based reasoning,geographic information systems,visual databases,spatial data,cbr approach,reasoning algorithm,geographic cases,geographic case-based reasoning,geographical data mining,data mining,spatial relationships,case based reasoning,case base reasoning | Geospatial analysis,Spatial analysis,Spatial similarity,Data mining,Geographic information system,Computer science,Spatial data mining,Rough set,Artificial intelligence,Case-based reasoning,Machine learning | Conference |
Volume | ISSN | ISBN |
5 | 2153-6996 | 978-1-4244-3395-7 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
5 | ||