Knowledge-based association rule mining using AND–OR taxonomies

Abstract

We introduce a knowledge-based approach to mine generalized association rules which is sound and interactive. Proposed mining is sound because our scheme uses knowledge for mining for only those concepts that are of interest to the user. It is interactive because we provide a user controllable parameter with the help of which user can interactively mine. For this, we use a taxonomy based on functionality, and a restricted way of generalization of the items. We call such a taxonomy A O taxonomy and the corresponding generalization A O generalization. We claim that this type of generalization is more meaningful since it is based on a semantic-grouping of concepts. We use this knowledge to naturally exploit the mining of interesting negative association rules. We define the interestingness of association rules based on the level of the concepts in the taxonomy. We give an efficient algorithm based on A O taxonomy which not only derives generalized association rules, but also accesses the database only once.