Abstract
In the field of knowledge discovery in databases (KDD), the effectiveness of association rules is important. Association rules are a technique of data mining, wherein we identify the relationship between one item to another. For mining, the association rules Apriori algorithm is widely used. The idea of the Apriori algorithm is to find the frequent sets from a transactional database. Through the frequent sets, association rules are obtained, and these rules must satisfy the minimum confidence threshold. This paper presents an improved method for deciding an optimum minimum support threshold and minimum confidence threshold, pruning of rules based on a contingency table, and finally the decision about whether to go for lift or confidence to get rid of uninteresting, misleading, and confusing association rules.
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Veer, A., Gurav, M., Dange, S., Chandgude, S., Wangikar, V. (2022). Apriori Algorithm with Dynamic Parameter Selection and Pruning of Misleading Rules. In: Zhang, YD., Senjyu, T., So-In, C., Joshi, A. (eds) Smart Trends in Computing and Communications. Lecture Notes in Networks and Systems, vol 286. Springer, Singapore. https://doi.org/10.1007/978-981-16-4016-2_59
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DOI: https://doi.org/10.1007/978-981-16-4016-2_59
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