Abstract
In this paper, we present a novel approach for finding association rules from locally frequent itemsets using rough set and boolean reasoning. The rules mined so are termed as local association rules. The efficacy of the proposed approach is established through experiment over retail dataset that contains retail market basket data from an anonymous Belgian retail store.
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Mazarbhuiya, F.A., Abulaish, M., Mahanta, A.K., Ahmad, T. (2009). Mining Local Association Rules from Temporal Data Set. In: Chaudhury, S., Mitra, S., Murthy, C.A., Sastry, P.S., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2009. Lecture Notes in Computer Science, vol 5909. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11164-8_41
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DOI: https://doi.org/10.1007/978-3-642-11164-8_41
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