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Posted by / 06-Nov-2018 00:57

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.

proposed the Fast-UPdated (FUP) algorithm [8] to maintain and update the discovered information with transaction insertion.

It divides the discovered frequent itemsets from the original database and all itemsets in the inserted transactions into four cases.

Some HUM algorithms have been proposed with transaction insertion [15–17].

The original database is still, however, required to be rescanned for maintaining and updating the HUIs in some cases.

The FP-growth algorithm [5] was the first algorithm to efficiently mine the frequent itemsets without candidate generation.

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

Both the level-wise or pattern-growth approaches can only handle the static database in batch mode.

When transactions are changed in the database, new information may arise and old ones may become invalid.

It may be thought of as an extension of frequent-itemset mining by considering the sold quantities and profits of the items.

The utility of an itemset can be measured in terms of quantity and profit, which can be defined by user preference.

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The procedures for four cases are, respectively, designed to maintain and update the discovered frequent itemsets.