Coupling Two Complementary Knowledge Discovery Systems

Lawrence B. Holder and Diane J. Cook

Most approaches to knowledge discovery concentrate on either an attribute-value representation or a structural data representation. The discovery systems for these two representations are typically different, and their integration is non-trivial. We investigate a simpler integration of the two systems by coupling the two approaches. Our method first executes the structural discovery system on the data, and then uses these results to augment or compress the data before being input to the attribute-value-based system. We demonstrate this strategy using the AutoClass attribute-value-based clustering system and the Subdue structural discovery system. The results of the demonstration show that coupling the two systems allows the discovery of knowledge imperceptible to either system alone.

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