Name
Abstract | ||
|---|---|---|
On the basis of analyzing the deficiencies of traditional spatial data mining, a framework for spatial data mining with uncertainty has been founded. Four key problems have been analyzed, including uncertainty simulation of spatial data with Monte Carlo method, spatial autocorrelation measurement, discretization of continuous data based on neighbourhood EM algorithm and uncertainty assessment of association rules. Meanwhile, the experiments concerned have been performed using the environmental geochemistry data gotten from Dexing, Jiangxi province in China. © 2006 IEEE. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICCIAS.2006.294245 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
null | Spatial analysis,Data mining,Discretization,Monte Carlo method,Computer science,Expectation–maximization algorithm,Sensitivity analysis,Uncertainty analysis,Association rule learning,Neighbourhood (mathematics),Artificial intelligence,Machine learning | Conference |
Volume | Issue | ISSN |
1 | null | null |
ISBN | Citations | PageRank |
1-4244-0605-6 | 2 | 0.63 |
References | Authors | |
4 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Binbin He | 1 | 8 | 2.29 |
| Chen Cuihua | 2 | 2 | 0.96 |