An Efficient Algorithm for Mining High Utility Quantitative Itemsets

• Published in: 2019 International Conference on Data Mining Workshops (ICDMW) pp. 1005 - 1012
• Main Authors: Li, Chia-Hua, Wu, Cheng-Wei, Huang, JianTao, Tseng, Vincent S.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2019
• Subjects: Classification algorithms; Computer science; Dairy products; Data mining; high utility itemset mining; high utility quantitative itemset mining; Itemsets; quantitative itemset mining; Utility mining
• ISSN: 2375-9259
• DOI: 10.1109/ICDMW.2019.00145

Mining high utility quantitative itemsets (HUQIs) is now a novel research topic in data mining field, which consists of discovering sets of items having a high utility (e.g. high profit) and providing information about quantities of items in each itemset. In market analysis, it could supply for decision-makers that shopping behavior could bring high profit to the company. For example, the customers purchase M to N units of a product A and purchase P to Q units of a product B at the same time. However, mining HUQIs using existing algorithms remains very computationally expensive and makes the results hard to be utilized by users. In view of this, we propose a novel algorithm named HUQI-Miner (High Utility Quantitative Itemsets Miner) for efficiently mining HUQIs in databases. Experimental results on both real and synthetic datasets show that HUQI-Miner outperforms the state-of-the-art algorithms in terms of both execution time and memory usage.