Abstract
High utility item-set mining (HUIM) is an important mining technique in the Data mining field. HUIM produces more beneficial item-sets and the relationship among these item-sets, to make various decision making activities. However, for effective decision making in businesses, factors like discounts and frequency patterns should also be considered. This paper suggests a method in which a combination of low frequent item-sets with high-frequency item-sets to provide association rules considering factors like discount and frequency patterns. A numerical example is used to clarify the method. Moreover, experimental results on real world data sets are done to prove the utility of the algorithm.
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Agarwal, R., Gautam, A., Rai, A., Karatangi, S.V., Verma, E. (2023). An Approach to Mine Low-Frequency Item-Sets. In: Agrawal, R., Mitra, P., Pal, A., Sharma Gaur, M. (eds) International Conference on IoT, Intelligent Computing and Security. Lecture Notes in Electrical Engineering, vol 982. Springer, Singapore. https://doi.org/10.1007/978-981-19-8136-4_7
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DOI: https://doi.org/10.1007/978-981-19-8136-4_7
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