Abstract: Abstract: The Association Rule Mining is the traditional mining technique which identifies the frequent itemsets from the databases and this technique generates the rules by considering the each items. The traditional association rule mining fails to obtain the infrequent itemsets with higher profit. Since association rule mining technique treats all the items in the database equally by considering only the presence of items within the transaction. The above problem can be solved using the Utility Mining technique. The Utility Mining technique identifies the product combinations with high profit but low frequency itemsets in the transactional database. Hiding High Utility Itemsets (HUIs) is the main challenges faced in the utility mining. The proposed MHIS algorithm computes the sensitive itemsets by utilizing the user defined threshold value. In order to hide the sensitive itemsets, the frequency value of the itemsets is changed. If the utility values of the items are same, the algorithm selects the accurate items and then the frequency values of the selected items are modified. The proposed algorithm MHIS reduces the computational complexity as well as improves the hiding performance of the itemsets.
Keywords: Hiding High Utility Itemsets (HUIs), MHIS, Utility Mining technique, Association Rule Mining (ARM).