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. © 2009 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Mazarbhuiya, F. A., Abulaish, M., Mahanta, A. K., & Ahmad, T. (2009). Mining local association rules from temporal data set. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5909 LNCS, pp. 255–260). https://doi.org/10.1007/978-3-642-11164-8_41
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