Name
Abstract | ||
|---|---|---|
. As the attribute spaces available to geography expand, new challenges are posed in comprehending and analysing data. This
article introduces two complementary approaches to analysis that show promise in addressing data with high attribute dimensionality:
inductive learning and visualisation. Whilst neither of these techniques are yet as robust or generally available as many
accepted parametric techniques, they are nevertheless able to provide insight, and in the case of inductively-based classifiers
and approximation methods, have been shown to outperform traditional approaches in some geographic settings. Some problems
with parametric inferential statistics are briefly mentioned, followed by descriptions of inductive and visual analysis methods,
and some of the important research that remains to be done before they can take a more prominent role. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1007/s101090050033 | Journal of Geographical Systems |
Keywords | Field | DocType |
Key words: High-dimensionality,visualisation,machine learning,spatial analysis,classification | Econometrics,Spatial analysis,Geographic information system,Data processing,Visualization,Curse of dimensionality,Parametric statistics,Artificial intelligence,Statistical inference,Geography,Machine learning | Journal |
Volume | Issue | Citations |
2 | 1 | 3 |
PageRank | References | Authors |
0.48 | 8 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mark Gahegan | 1 | 571 | 55.38 |