Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Fournier-Viger, Philippe | en_US |
dc.contributor.author | Zida, Souleymane | en_US |
dc.contributor.author | Lin, Jerry Chun-Wei | en_US |
dc.contributor.author | Wu, Cheng-Wei | en_US |
dc.contributor.author | Tseng, Vincent S. | en_US |
dc.date.accessioned | 2017-04-21T06:49:37Z | - |
dc.date.available | 2017-04-21T06:49:37Z | - |
dc.date.issued | 2016 | en_US |
dc.identifier.isbn | 978-3-319-41920-6 | en_US |
dc.identifier.isbn | 978-3-319-41919-0 | en_US |
dc.identifier.issn | 0302-9743 | en_US |
dc.identifier.uri | http://dx.doi.org/10.1007/978-3-319-41920-6_15 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/136265 | - |
dc.description.abstract | 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. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | Pattern mining | en_US |
dc.subject | High-utility itemset | en_US |
dc.subject | Closed itemset | en_US |
dc.title | EFIM-Closed: Fast and Memory Efficient Discovery of Closed High-Utility Itemsets | en_US |
dc.type | Proceedings Paper | en_US |
dc.identifier.doi | 10.1007/978-3-319-41920-6_15 | en_US |
dc.identifier.journal | MACHINE LEARNING AND DATA MINING IN PATTERN RECOGNITION (MLDM 2016) | en_US |
dc.citation.volume | 9729 | en_US |
dc.citation.spage | 199 | en_US |
dc.citation.epage | 213 | en_US |
dc.contributor.department | 資訊工程學系 | zh_TW |
dc.contributor.department | Department of Computer Science | en_US |
dc.identifier.wosnumber | WOS:000386510300015 | en_US |
dc.citation.woscount | 0 | en_US |
Appears in Collections: | Conferences Paper |