標題: Mining Minimal High-Utility Itemsets
作者: Fournier-Viger, Philippe
Lin, Jerry Chun-Wei
Wu, Cheng-Wei
Tseng, Vincent S.
Faghihi, Usef
資訊工程學系
Department of Computer Science
關鍵字: Utility mining;High-utility itemsets;Minimal itemsets
公開日期: 2016
摘要: Mining high-utility itemsets (HUIs) is a key data mining task. It consists of discovering groups of items that yield a high profit in transaction databases. A major drawback of traditional high-utility itemset mining algorithms is that they can return a large number of HUIs. Analyzing a large result set can be very time-consuming for users. To address this issue, concise representations of high-utility itemsets have been proposed such as closed HUIs, maximal HUIs and generators of HUIs. In this paper, we explore a novel representation called the minimal high utility itemsets (MinHUIs), defined as the smallest sets of items that generate a high profit, study its properties, and design an efficient algorithm named MinFHM to discover it. An extensive experimental study with real-life datasets shows that mining MinHUIs can be much faster than mining other concise representations or all HUIs, and that it can greatly reduce the size of the result set presented to the user.
URI: http://dx.doi.org/10.1007/978-3-319-44403-1_6
http://hdl.handle.net/11536/136436
ISBN: 978-3-319-44403-1
978-3-319-44402-4
ISSN: 0302-9743
DOI: 10.1007/978-3-319-44403-1_6
期刊: DATABASE AND EXPERT SYSTEMS APPLICATIONS, DEXA 2016, PT I
Volume: 9827
起始頁: 88
結束頁: 101
Appears in Collections:Conferences Paper