Knowledge mining by imprecise querying: a classification-based approach

Knowledge mining is the process of discovering knowledge that is hitherto unknown. An approach to knowledge mining by imprecise querying that utilizes conceptual clustering techniques is presented. The query processor has both a deductive and an inductive component. The deductive component finds precise matches in the traditional sense, and the inductive component identifies ways in which imprecise matches may be considered similar. Ranking on similarity is done by using the database taxonomy, by which similar instances become members of the same class. Relative similarity is determined by depth in the taxonomy. The conceptual clustering algorithm, its use in query processing, and an example are presented.>