標題: Mining High-Utility Itemsets with Various Discount Strategies
作者: Lin, Jerry Chun-Wei
Gan, Wensheng
Fournier-Viger, Philippe
Hong, Tzung-Pei
Tseng, Vincent S.
資訊工程學系
Department of Computer Science
公開日期: 2015
摘要: In recent years, mining high-utility itemsets (HUIs) has become as a key topic in data mining. However, most of the developed algorithms assume the unrealistic situations that unit profits of items remain unchanged over time. But in real-life situations, the profit of an item or itemset varies as a function of cost prices, sales prices and sales strategies. In this paper, a novel framework for mining HUIs with two algorithms under various Discount strategies (HUID) are introduced. HUID-tp is based on various discount strategies and a novel downward closure property to mine the complete set of HUIs. HUID-Miner is an algorithm relying on a compact data structure (Positive-and-Negative Utility-list, PNU-list) and new pruning strategies to efficiently discover HUIs without candidate generation, while considerably reducing the size of the search space. Furthermore, a strategy named Estimated Utility Co-occurrence Strategy which stores the relationships between 2-itemsets is also adopted in the proposed improvement HUID-EMiner algorithm to speed up computation. An extensive experimental study carried on several real-life datasets shows the performance of the proposed algorithms.
URI: http://hdl.handle.net/11536/136327
ISBN: 978-1-4673-8273-1
期刊: PROCEEDINGS OF THE 2015 IEEE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (IEEE DSAA 2015)
起始頁: 742
結束頁: 751
Appears in Collections:Conferences Paper