Spatial Data Mining Implementation: Alternatives and Performances

Spatial data mining requires the analysis of the interactions in space. These interactions can be materialized using distance tables, reducing spatial data mining to multi-table analysis. However, conventional data mining algorithms consider only one input table where each row is an observation to analyze. Simple relational joins between these tables does not resolve the problem and mislead the results because of the multiple counting of observations. We propose three alternatives of multi-table data mining in the context of spatial data mining. The first makes a hard modification in the conventional algorithm in order to consider those tables. The second is an optimization of the first approach. It pre-computes all join operations and adapts the conventional algorithm. The third re-organizes data into a unique table by completing -not joiningthe target table using the existing data in the other tables, then applies any standard data mining algorithm without modification. This article presents these three alternatives. It describes their implementation for classification algorithms and compares their performances.