Name
Abstract | ||
|---|---|---|
Spatial Data Mining can mine automatically or semi-automatically unknown, creditable, effective, integrative or schematic knowledge which can be understood from the increasingly complex spatial database and enhance the ability of interpreting data to generate useful knowledge. There are large amounts of data existing in the database of Government GIS. However, a great deal of the data is idle, which has caused a huge waste of data due to rarely effectively utilization in practice. It is very necessary to deal with the task of data mining based on Government GIS. In this paper, an example on land utilization and land cover in a certain region of Guizhou Province was presented to describe the course of spatial data mining. There into, a derived star-type model was used to organize the raster data to form multi dimension data set. Under these conditions, clustering method was utilized to carry out data mining aiming at the raster data. Based on corresponding analyses, users can find out which types of vegetation were suitable for being cultivated in this region by using related knowledge in a macroscopic view. Therefore, feasible service information could be provided to promote economic development in the region. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/FSKD.2009.273 | FSKD (1) |
Keywords | Field | DocType |
raster data,data mining,complex spatial database,multi dimension data,useful knowledge,certain region,schematic knowledge,multi dimension data set,government gis,related knowledge,raster data mining,spatial data mining,classification algorithms,data models,government,spatial database,geographic information systems,clustering algorithms | Raster data,Geographic information system,Data mining,Data modeling,Data stream mining,Computer science,Cluster analysis,Statistical classification,Spatial database,Spatial data infrastructure | Conference |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Li | 1 | 16 | 2.75 |
| Lihong Shi | 2 | 3 | 1.47 |
| Jiping Liu | 3 | 11 | 6.00 |
| Liang Wang | 4 | 1567 | 158.46 |