Market basket analysis, one of the examples for affinity analysis gained its importance as a research area by trying to provide the retailer with the information about which products will go together in a sale. Capitalizing over this concept the Association Rule mining, found rules that could predict the occurrence of an item in a transaction based on the occurrences of other items in the transaction. Earlier methods that tried to mine association rules considered the items as a binary value irrespective of their purchase quantity in a transaction. Utility Mining overcomes this particular issue, by considering the unit cost of an item may vary from other items and the importance of the transaction based on the total cost rather than the number of items present in the transaction. The main objective of Utility Mining is to find the itemsets that have high utility values, the higher the utility value the more the importance it possess, the utility of an itemset is arrived at by considering profit, quantity, cost etc. The whole idea of HUI itself is an extension of frequent itemset mining and is considered to be hard to mine. The complexity of mining HUI rests on its very nature that it lacks inherent structural properties. The large search space always proves to be a delimiter in finding HUI. Many methods have been proposed to manage this search space. This paper proposes a method that could mine HUI using an enhanced pruning strategy. Copyright © 2018 American Scientific Publishers All rights.