Incremental Updates of Discovered Multi-Level Association Rules

Update of the single- and multi-level association rules discovered in large databases is inherently costly. The straight forward approach of re-running the discovery algorithm on the entire updated database to re-discover the association rules is not cost-effective. An incremental algorithm FUP have been proposed for the update of discovered single-level association rules. In this study, we have shown that the incremental technique in FUP can be generalized to other data mining systems. An efficient algorithm MLU p has been proposed for the updating of discovered multi-level association rules. Our performance study shows that MLU p has a superior performance over the representative mining algorithm such as ML-T2 in updating discovered multi-level association rules.