SPATIAL AND GEOGRAPHIC DM

data mining: opportunities and challenges
Chapter XX - Critical and Future Trends in Data Mining A Review of Key Data Mining Technologies/Applications
Data Mining: Opportunities and Challenges
by John Wang (ed) 
Idea Group Publishing 2003
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Aside from statistical or numeric data, it is also important to consider information that is of an entirely different kind-i.e., spatial and geographic data that could contain information about astronomical data, natural resources, or even orbiting satellites and spacecraft that transmit images of earth from out in space. Much of this data is image-oriented and can represent a great deal of information if properly analyzed and mined (Miller & Han, 2001). Analyzing spatial and geographic data includes such tasks as understanding and browsing spatial data, uncovering relationships between spatial data items (and also between non-spatial and spatial items), and also analysis using spatial DBs and spatial knowledge bases. The applications of these would be useful in such fields as remote sensing, medical imaging, navigation, and related uses. Some of the techniques and data structures that are used to analyze spatial and related types of data include the use of spatial warehouses, spatial data cubes, and spatial On Line Analytic Processing (OLAP). Spatial data warehouses can be defined as those that are subject-oriented, integrated, nonvolatile, and time-variant (Han, Kamber, & Tung, 2001). Aside from the implementation of data warehouses for spatial data, there is also the issue of analyses that can be done on the data. Some of the analyses that can be done include association analysis, clustering methods, and the mining of raster DBs. There have been a number of studies conducted on spatial DM (Bedard, Merritt, & Han 2001; Han, Kamber & Tung, 1998; Han, Koperski, & Stefanovic, 1997; Han, Stefanovic, & Koperski, 1998; Koperski, Adikary, & Han, 1996; Koperski & Han, 1995; Koperski, Han, & Marchisio, 1999; Koperski, Han, & Stefanovic, 1998; Tung, Hou, & Han, 2001).

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Data Mining(c) Opportunities and Challenges
Data Mining: Opportunities and Challenges
ISBN: 1591400511
EAN: 2147483647
Year: 2003
Pages: 194
Authors: John Wang

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