High average-utility itemsets mining (HAUIM) is a key data mining task, which aims at discovering high average-utility itemsets (HAUIs) by taking itemset length into account in transactional databases. Most of these algorithms only consider a single minimum utility threshold for identifying the HAUIs. In this paper, we address this issue by introducing two phase algorithm with pruning strategy in which the task of mining HAUIs is done with multiple minimum average utility thresholds , where the user may assign a distinct minimum average-utility threshold to each item or itemset.

Frequent itemsets ,minimum supports, utility mining,high utility mining

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