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.
CITATION STYLE
Fournier-Viger, P., Zida, S., Lin, J. C. W., Wu, C. W., & Tseng, V. S. (2016). EFIM-closed: Fast and memory efficient discovery of closed high-utility itemsets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9729, pp. 199–213). Springer Verlag. https://doi.org/10.1007/978-3-319-41920-6_15
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