Visual data mining of raster data: a volume-rendering-based hierarchical approach

Developments of raster data capture technologies and demands from application fields call for advanced raster data analysis methods. Visual data mining that involves human's visual analytical capability in data analysis attracts attention in recent years. Raster datasets usually have large amount of pixels, which may cause serious clotting problem in visualization and thus challenges visual data mining. The research reported here mainly focuses on this problem and tries to construct a hierarchical framework for visual data mining of raster data. In the hierarchical structure, the first level uses volume rendering to visualize the whole raster dataset in attribute space, which can greatly reduce the impact of clotting. To avoid the loss of subtle patterns, the second level makes use of parallel coordinates plot to reflect detailed attribute information. This hierarchical structure ensures that both global and local patterns embedded in data can be detected. In both levels, visualizations of attribute space are linked with that of geographic space. Software prototype was developed and then applied to find small clusters that may relate to possible soil types. Case study result demonstrated the effectiveness of this proposed approach.

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