Speeding Up Multi-Relational Data Mining

We present a general approach to speeding up a family of multi-relational data mining algorithms that construct and use selection graphs to obtain the information needed for building predictive models (e.g., decision tree classifiers) from relational database. Preliminary results of our experiments suggest that the proposed method can yield 1-2 orders of magnitude reductions in the running time of such algorithms without any deterioration in the quality of results. The proposed modifications enhance the applicability of multi-relational data mining algorithms to significantly larger relational databases that would otherwise be not feasible in practice