From data to global generalized knowledge
The attribute-oriented induction (AOI) is a useful data mining method that extracts generalized knowledge from relational data and user's background knowledge. The method uses two thresholds, the relation threshold and attribute threshold, to guide the generalization process, and output generalized knowledge, a set of generalized tuples which describes the major characteristics of the target relation. Although AOI has been widely used in various applications, a potential weakness of this method is that it only provides a snapshot of the generalized knowledge, not a global picture. When thresholds are different, we would obtain different sets of generalized tuples, which also describe the major characteristics of the target relation. If a user wants to ascertain a global picture of induction, he or she must try different thresholds repeatedly. That is time-consuming and tedious. In this study, we propose a global AOI (GAOI) method, which employs the multiple-level mining technique with multiple minimum supports to generate all interesting generalized knowledge at one time. Experiment results on real-life dataset show that the proposed method is effective in finding global generalized knowledge.
generalized knowledge attribute-oriented induction data mining multiple-level mining