EHNL: An efficient algorithm for mining high utility itemsets with negative utility value and length constraints

Abstract

High utility itemsets (HUIs) mining is one of the emerging topics in frequent itemset mining (FIM). HUIs mining provides more informative and actionable information compared to FIM. Although many HUIs mining algorithms have been proposed in recent years. They incur the problem of generating a large number of candidate itemsets and most of the generated itemsets are tiny in size which degrade mining performance and action-ability. Apart from these problems, most of the algorithms work only with positive utility value. To overcome these issues, we propose an algorithm named EHNL (Efficient High utility itemsets mining with Negative utility and Length constraints). Although negative utility and constraint-based mining are commonly seen in real-world applications, mining HUIs with negative utility and length constraints has not yet been proposed in literature. Most of the traditional algorithms suffer from multiple dataset scanning problem. To reduce the scanning cost, we utilize dataset projection and transaction merging techniques. To enhance the performance of the proposed algorithm, we utilize sub-tree based pruning technique. To check the efficiency of utilized techniques, the variations of the proposed algorithm named EHNL(RSUP) and EHNL(TM) are introduced. The experimental results show that variants of the proposed algorithm mine the HUIs efficiently.