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 |