Evaluating the Role of Background Knowledge in Enhancing Knowledge Discovery in Databases

Venkateswarlu Kolluri

In the field of Knowledge Discovery in Databases (KDD), background knowledge is usually available in the form of taxonomies (is-a hierarchies) over the features and the corresponding feature-value hierarchies. Using the RL inductive rule learning system as a test bed, we are trying to evaluate the effectiveness of such background knowledge in enhancing the KDD process. The improvements in the learned concept definitions are evaluated based on the learnt rule set’s predictive power and simplicity.

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