Using branch-grafted R-trees for spatial data mining

Spatial data mining is a process of extraction of implicit information, such as weather patterns around latitudes, spatial features in a region, etc., with a goal of knowledge discovery. The work reported here is based on our earlier work on branch-grafted R trees. We have taken a bottom-up approach in our research: from efficient spatial data structure (i.e., branch-grafted R tree implementation), to efficient data access methods, and finally, to effective spatial data mining. Since previous experiments have shown that there are significant advantages of using branch-grafted implementation, this bottom-up approach exploits the performance advantages of the branch-grafted R-trees.