In this paper, we handle a new kind of patterns named high on-shelf utility itemsets, which considers not only individual profit and quantity of each item in a transaction but also common on-shelf time periods of a product combination. We propose a three-scan mining approach to effectively and efficiently discover high on-shelf utility itemsets. The proposed approach adopts an itemset-generation mechanism to prune redundant candidates early and to systematically check the itemsets from transactions. The experimental results on synthetic datasets also show the proposed approach has a good performance. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Lan, G. C., Hong, T. P., & Tseng, V. S. (2010). A three-scan algorithm to mine high on-shelf utility itemsets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5991 LNAI, pp. 351–358). https://doi.org/10.1007/978-3-642-12101-2_36
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