標題: EFIM-Closed: Fast and Memory Efficient Discovery of Closed High-Utility Itemsets
作者: Fournier-Viger, Philippe
Zida, Souleymane
Lin, Jerry Chun-Wei
Wu, Cheng-Wei
Tseng, Vincent S.
資訊工程學系
Department of Computer Science
關鍵字: Pattern mining;High-utility itemset;Closed itemset
公開日期: 2016
摘要: Discovering high-utility temsets in transaction databases is a popular data mining task. A limitation of traditional algorithms is that a huge amount of high-utility itemsets may be presented to the user. To provide a concise and lossless representation of results to the user, the concept of closed high-utility itemsets was proposed. However, mining closed high-utility itemsets is computationally expensive. To address this issue, we present a novel algorithm for discovering closed high-utility itemsets, named EFIM-Closed. This algorithm includes novel pruning strategies named closure jumping, forward closure checking and backward closure checking to prune non-closed high-utility itemsets. Furthermore, it also introduces novel utility upper-bounds and a transaction merging mechanism. Experimental results shows that EFIM-Closed can be more than an order of magnitude faster and consumes more than an order of magnitude less memory than the previous state-of-art CHUD algorithm.
URI: http://dx.doi.org/10.1007/978-3-319-41920-6_15
http://hdl.handle.net/11536/136265
ISBN: 978-3-319-41920-6
978-3-319-41919-0
ISSN: 0302-9743
DOI: 10.1007/978-3-319-41920-6_15
期刊: MACHINE LEARNING AND DATA MINING IN PATTERN RECOGNITION (MLDM 2016)
Volume: 9729
起始頁: 199
結束頁: 213
Appears in Collections:Conferences Paper