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THAPE: A Tunable Hybrid Associative Predictive Engine Approach for Enhancing rule interpretability in Association rule Learning for the Retail Sector

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Computers, Materials & Continua 2024年第6期79卷 4995-5015页

作者： Monerah Alawadh Ahmed Barnawi Information Technology Department King Abdulaziz UniversityJeddah21589Saudi Arabia

Association rule learning(ARL)is a widely used technique for discovering relationships within ***,it often generates excessive irrelevant or ambiguous ***,post-processing is crucial not only for removing irrelevant or redundant rules but also for uncovering hidden associations that impact other ***,several post-processing methods have been proposed,each with its own strengths and *** this paper,we propose THAPE(Tunable Hybrid Associative Predictive Engine),which combines descriptive and predictive *** leveraging both techniques,our aim is to enhance the quality of analyzing generated *** includes removing irrelevant or redundant rules,uncovering interesting and useful rules,exploring hidden association rules that may affect other factors,and providing backtracking ability for a given *** proposed approach offers a tailored method that suits specific goals for retailers,enabling them to gain a better understanding of customer behavior based on factual transactions in the target *** applied THAPE to a real dataset as a case study in this paper to demonstrate its *** this application,we successfully mined a concise set of highly interesting and useful association *** of the 11,265 rules generated,we identified 125 rules that are particularly relevant to the business *** identified rules significantly improve the interpretability and usefulness of association rules for decision-making purposes.

关键词： Association rule learning post-processing predictive machine learning rule interpretability

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