Abstract
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.
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Lan, GC., Hong, TP., Tseng, V.S. (2010). A Three-Scan Algorithm to Mine High On-Shelf Utility Itemsets. In: Nguyen, N.T., Le, M.T., Świątek, J. (eds) Intelligent Information and Database Systems. ACIIDS 2010. Lecture Notes in Computer Science(), vol 5991. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12101-2_36
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DOI: https://doi.org/10.1007/978-3-642-12101-2_36
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