Abstract
High-utility itemset mining (HUIM) is an important data mining task with wide applications. In this paper, we propose a novel algorithm named EFIM (EFficient high-utility Itemset Mining), which introduces several new ideas to more efficiently discovers high-utility itemsets both in terms of execution time and memory. EFIM relies on two upper-bounds named sub-tree utility and local utility to more effectively prune the search space. It also introduces a novel array-based utility counting technique named Fast Utility Counting to calculate these upper-bounds in linear time and space. Moreover, to reduce the cost of database scans, EFIM proposes efficient database projection and transaction merging techniques. An extensive experimental study on various datasets shows that EFIM is in general two to three orders of magnitude faster and consumes up to eight times less memory than the state-of-art algorithms d\(^2\)HUP, HUI-Miner, HUP-Miner, FHM and UP-Growth+.
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This work is financed by a National Science and Engineering Research Council (NSERC) of Canada research grant.
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Zida, S., Fournier-Viger, P., Lin, J.CW., Wu, CW., Tseng, V.S. (2015). EFIM: A Highly Efficient Algorithm for High-Utility Itemset Mining. In: Sidorov, G., Galicia-Haro, S. (eds) Advances in Artificial Intelligence and Soft Computing. MICAI 2015. Lecture Notes in Computer Science(), vol 9413. Springer, Cham. https://doi.org/10.1007/978-3-319-27060-9_44
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DOI: https://doi.org/10.1007/978-3-319-27060-9_44
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