Business Intelligence

Mining Positive and Negative Association Rules: An Approach for Binary Trees

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Executive Summary

Mining association rules and especially the negative ones has received a lot of attention and has been proved to be useful in the real world. In this work, a set of algorithms for finding both positive and Negative Association Rules (NAR) in databases is presented. A variant of the Apriori, traditional association rules algorithm, is achieved using support and confidence in order to discover two types of NAR; the Confined Negative association Rules (CNR), and the Generalized Negative Association Rules (GNAR).

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