Incremental updating algorithm association rules No credit card free phone sex chat
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].
Association-rule mining (ARM) [1–3] from a transactional database is a fundamental task for revealing the relationships among items.
The Apriori  was the first algorithm to mine the association rules in a level-wise way.
The enumeration tree and the relationships between 2-itemsets are also adopted in the proposed algorithm to speed up the computations.
Several experiments are conducted to show the performance of the proposed algorithm in terms of runtime, memory consumption, and number of generated patterns.
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When the itemsets are small in the original database (support ratio is lower than minimum support threshold) but large in the new database (support ratio is larger than or equal to the minimum support threshold), the original database is required to be rescanned to find the actual occurrence frequencies of the small itemsets in the original database.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.A pattern with highly frequency may not be interested if it cannot bring highly profit for retailer.
For example, a sale of diamonds may occur less frequently than that of clothing in a department store, but the former gives a much higher profit per unit sold than the latter.
It uses generate-and-test mechanism to find the candidate itemsets and then derive the frequent itemsets based on the minimum support threshold.