抄録
Mining association rules from a large business database, has been recognized as an important topic in the data mining community. A method that can help the analysis of associations is the use of classification ontology (taxonomy) and the setting of parameter constraints, such as minimum support. In real world applications, however, the classification ontology cannot be kept static while new transactions are continuously added into the original database, and the analysts may also need to set a different support constraint from the original one while formulating a new query in discovering real informative rules. Efficiently updating the discovered generalized association rules to reflect the change with classification ontology, support constraint and new added transactions is a crucial task. In this paper, we examine this problem and propose a novel algorithm, called IMA_HOSU, which can incrementally update the discovered generalized association rules when the classification ontology updates with a renewed minimum support. Empirical evaluations show that our algorithm is faster than applying the contemporary generalized associations mining algorithms to the whole updated database.
