An efficient algorithm for mining high utility quantitative itemsets

Chia Hua Li, Cheng Wei Wu, Jian Tao Huang, Vincent S. Tseng

研究成果: Conference contribution同行評審

7 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
編輯Panagiotis Papapetrou, Xueqi Cheng, Qing He
發行者IEEE Computer Society
頁面1005-1012
頁數8
ISBN(電子)9781728146034
DOIs
出版狀態Published - 11月 2019
事件19th IEEE International Conference on Data Mining Workshops, ICDMW 2019 - Beijing, China
持續時間: 8 11月 201911 11月 2019

出版系列

名字IEEE International Conference on Data Mining Workshops, ICDMW
2019-November
ISSN(列印)2375-9232
ISSN(電子)2375-9259

Conference

Conference19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
國家/地區China
城市Beijing
期間8/11/1911/11/19

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