High utility itemsets mining (HUIM) has become an emerging topic in association rules.Some algorithms based on vertical data structure have been used for mining high utility itemsets(HUIs)

and the main advantage of the algorithms are to maintain transaction and utility information of itemsets in some utility lists(ULs).The transactions of superset of an itemsets can be calculated by its subset doing an intersection.These algorithms are very effective in sparse datasets.However

in the dense datasets

a problem is that:too many transactions maintained in ULs

not only required a lot of memory space

but also affected the runtime when computing the upper bound of utility in order to prune search space.Few of existing HUIM algorithm focused on dense datasets and it often need to set a high minimum threshold utility which affect the running efficiency of the algorithm.To solve this problem

propose a new algorithm D-HUI(mining High Utility Itemsets using Diffsets) and a new data structure

namely Itemset Lists (ILs).Introduce the concept of diffsets in the HUIM.Calculate upper bound of utility by using diffsets of transaction for pruning search space.The runtime and memory consumption are reduced

and the running efficiency of the algorithm is improved.Experimental results show that the proposed algorithm in the dense datasets outperforms state-of-the-art algorithms in terms of both running time and memory consumption.