FHUQI-Miner: Fast high utility quantitative itemset mining
Peer reviewed, Journal article
Accepted version
Permanent lenke
https://hdl.handle.net/11250/2990178Utgivelsesdato
2021Metadata
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Originalversjon
Nouioua, M., Fournier-Viger, P., Wu, C.-W., Lin, J. C.-W., & Gan, W. (2021). FHUQI-Miner: Fast high utility quantitative itemset mining. Applied Intelligence, 51(10), 6785–6809. 10.1007/s10489-021-02204-wSammendrag
High utility itemset mining is a popular pattern mining task, which aims at revealing all sets of items that yield a high profit in a transaction database. Although this task is useful to understand customer behavior, an important limitation is that high utility itemsets do not provide information about the purchase quantities of items. Recently, some algorithms were designed to address this issue by finding quantitative high utility itemsets but they can have very long execution times due to the larger search space. This paper addresses this issue by proposing a novel efficient algorithm for high utility quantitative itemset mining, called FHUQI-Miner (Fast High Utility Quantitative Itemset Miner). It performs a depth-first search and adopts two novel search space reduction strategies, named Exact Q-items Co-occurrence Pruning Strategy (EQCPS) and Range Q-items Co-occurrence Pruning Strategy (RQCPS). Experimental results show that the proposed algorithm is much faster than the state-of-art HUQI-Miner algorithm on sparse datasets.
Beskrivelse
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10489-021-02204-w