Affiliations: School of Computer Science and Engineering, North Minzu University, Yinchuan, China
Correspondence:
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Corresponding author. Meng Han, School of Computer Science and Engineering, North Minzu University, Ningxia, China. E-mails: [email protected], [email protected].
Abstract: High utility quantitative itemsets (HUQI) mining is a new research topic in the field of data mining. It not only provides high utility itemset (HUI), but also provides quantitative information of individual item in the itemset. HUQI can provide decision makers with information about items and their purchase quantities. However, the currently proposed HUQI mining algorithms assume that the datasets are static. In order to solve this problem, an incremental quantitative utility list (IQUL) data structure is proposed to store item information, including item name, item number, transaction weight utility of item, each entry in the list stores the transaction identifier, the utility of the original data, the remaining utility, the utility of the incremental data, the remaining utility, and the sum of the utility and the remaining utility. When data is inserted, the item information will be updated. Based on IQUL, an incrementally updating HUQI (IHUQI) mining algorithm is proposed to mine HUQI on incremental update data. A large number of experiments on real datasets show that the IHUQI algorithm can effectively mine HUQI Experimental results show better performance on sparse datasets.
Keywords: Incremental mining, high utility quantitative itemsets, high utility itemsets, utility list, itemsets mining
