Abstract
Given a retail transactional database, the objective of high-utility pattern mining is to discover high-utility itemsets (HUIs), i.e., itemsets that satisfy a user-specified utility threshold. In retail applications, when purchasing a set of items (i.e., itemsets), consumers seek to replace or substitute items with each other to suit their individual preferences (e.g., Coke with Pepsi, tea with coffee). In practice, retailers, too, require substitutes to address operational issues like stockouts, expiration, and other supply chain constraints. The implication is that items that are interchangeably purchased, i.e., substitute goods, are critical to ensuring both user satisfaction and sustained retailer profits. In this regard, this work presents (i) an efficient model to identify HUIs containing substitute goods in place of items that require substitution, (ii) the SubstiTution-based Itemset indeX (STIX) to retrieve HUIs containing substitutes, and (iii) an experimental study to depict the benefits of the proposed approach w.r.t. a baseline method.
A. Mondal—With grief, this work reports the passing of Dr. Anirban Mondal in 2022. The authors of this work owe him a debt of gratitude for his guidance in preparing this manuscript.
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Mittal, R., Mondal, A., Reddy, P.K., Mohania, M. (2024). A Model for Retrieving High-Utility Itemsets with Complementary and Substitute Goods. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14645. Springer, Singapore. https://doi.org/10.1007/978-981-97-2242-6_27
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