Abstract
Many different algorithms are studied on association rules in the literature of data mining. Some researchers are now focusing on the application of association rules. In this paper, we will study one of the application called Item Selection for Marketing (ISM) with cross-selling effect consideration. The problem ISM is to find a subset of items as marketing items in order to boost the sales of the store. We prove a simple version of this problem is NP-hard. We propose an algorithm to deal with this problem. Experiments are conducted to show that the algorithms are effective and efficient.
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Wong, R.CW., Fu, A.WC. (2004). ISM: Item Selection for Marketing with Cross-Selling Considerations. In: Dai, H., Srikant, R., Zhang, C. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2004. Lecture Notes in Computer Science(), vol 3056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24775-3_53
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DOI: https://doi.org/10.1007/978-3-540-24775-3_53
Publisher Name: Springer, Berlin, Heidelberg
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