@inproceedings{67f1925534ac4ea896ab285b3c682ce8,
title = "EFIM-closed: Fast and memory efficient discovery of closed high-utility itemsets",
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.",
keywords = "Closed itemset, High-utility itemset, Pattern mining",
author = "Philippe Fournier-Viger and Souleymane Zida and Lin, {Jerry Chun Wei} and Wu, {Cheng Wei} and S. Tseng",
year = "2016",
month = jan,
day = "1",
doi = "10.1007/978-3-319-41920-6_15",
language = "English",
isbn = "9783319419190",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "199--213",
editor = "Petra Perner",
booktitle = "Machine Learning and Data Mining in Pattern Recognition - 12th International Conference, MLDM 2016, Proceedings",
address = "Germany",
note = "null ; Conference date: 16-07-2016 Through 21-07-2016",
}