Mining generalized association rules among items in the presence of taxonomies has been recognized as an important model in data mining. Earlier work on mining generalized association rules ignore the fact that the taxonomies of items cannot be kept static while new transactions are continuously added into the original database. How to effectively update the discovered generalized association rules to reflect the database change with taxonomy evolution and transaction update is a crucial task. In this paper, we examine this problem and propose a novel algorithm, called IDTE, which can incrementally update the discovered generalized association rules when the taxonomy of items is evolved with new transactions insertion to the database. Empirical evaluations show that our algorithm can maintain its performance even in large amounts of incremental transactions and high degree of taxonomy evolution, and is more than an order of magnitude faster than applying the best generalized associations mining algorithms to the whole updated database.
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