We present a general approach to speeding up a family of multi-relational data mining algorithms that using ID propagation to obtain the information needed for building decision tree classifier 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 accuracy of results. The proposed modifications enhance the applicability of decision tree algorithms to significantly relational databases that would otherwise be not feasible in practice.
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