Online mining of utility itemsets from data streams is one of the most interesting research issues in stream data mining. Although a number of relevant approaches have been proposed in recent years, they have the drawback of producing a large number of candidate itemsets for high-utility itemset mining. In this paper, an efficient algorithm, called MHUI-max (Mining High-Utility Itemsets based on LexTree-maxHTU), is proposed for mining high-utility itemsets from data streams with fewer candidates. Based on the framework of the MHUI-max algorithm, an effective representation of item information, called TID-list, and a new lexicographical tree-based data structure, called LexTree-maxHTU, has been developed to improve the efficiency of discovering high-utility itemsets with positive profits from data streams. Experimental results show that the proposed algorithm, MHUI-max, outperforms the existing approaches, MHUI-TID and THUI-Mine, for mining high-utility itemsets from data streams over transaction-sensitive sliding windows.
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