A Framework for Spatial Data Mining

Our framework for spatial data mining heavily depend on the efficient processing of neighborhood relations since the neighbors of many objects have to be investigated in a single run of a typical algorithm. Therefore, providing general concepts for neighborhood relations as well as an efficient implementation of these concepts w ill allow a tight integration of spatial data mining algorithms w ith a spatial database management system. This w ill speed up both, the development and the execution of spatial data mining algorithms. In this paper, w e define neighborhood graphs and paths and a small set of database primitives for their manipulation. We show that typical spatial data mining algorithms are w ell sup- ported by the proposed basic operations. For finding significant spatial patterns, only certain classes of paths "leading away" from a starting ob- ject are relevant. We discuss filters allow ing only such neighborhood paths which will significantly reduce the search space for spatial data mining algorithms. Furthermore, we introduce neighborhood indices to speed up the processing of our database primitives. We implemented the data- base primitives on top of a commercial spatial database management system. The effectiveness and efficiency of the proposed appr oach was evaluated by using an analytical cost model and an extensive experimental study on a geographic database.