Normally in a transaction database mining high utility itemsets indicates to the location of itemsets which is causing high utility like benefits. In spite of the fact that various important calculations have been proposed as of late, they bring about the issue of generating a huge amount of itemsets for mining to discover HUI. Mining is reduced by such an extended quantity as far as execution time and space complexity. When the database contains large amount of transactions, this condition may turn into mediocre. In this research paper, we account this concern by offering a state-of-the-art calculation named Depth Impurity Quality Index Pruned strategies which considers the complexity of sub-trees to more efficiently identify high-utility itemsets. It is an collection of common itemset which are used for mining and is significantly harder, inflexible. This is imputable to the absence of intrinsic organizational behaviour of HUI which could have worked. This paper suggests a high utility mining technique which make use of novel pruning approaches.The experimental outcomes disclose that the proposed method is exceptionally viable in killing unhopeful applicants in the database transactions.