An index Header_Table with a designed FP-growth mining algorithm is also proposed to find the corresponding paths of the items for deriving the frequent itemsets.Many algorithms have been, respectively, proposed to efficiently mine the association rules based on either the level-wise or pattern-growth mechanisms [2, 3].

The updated database is required to be processed to mine the updated information in batch mode, which is not suitable in practical applications.

To solve the above limitations of batch-mode algorithms [6, 7], Cheung et al.

Several algorithms have been proposed to mine HUIs in a static database [11–14].

As previously mentioned in ARM, it is also an important issue to design an algorithm to efficiently maintain and update the HUIs when data or transactions are frequently changed in the original database.

It uses generate-and-test mechanism to find the candidate itemsets and then derive the frequent itemsets based on the minimum support threshold.

The association rules are then revealed from the discovered frequent itemsets based on minimum confidence threshold.

High-utility mining was designed to solve the limitations of association-rule mining by considering both the quantity and profit measures.

Most algorithms of high-utility mining are designed to handle the static database.

For ARM, it only reveals the binary relationships among items.

The implicit factors such as profit or quantity are not concerned in ARM.

Association-rule mining (ARM) [1–3] from a transactional database is a fundamental task for revealing the relationships among items.