Abstract
Association rules are “if-then rules ”with two measures which quantify the support and confidence of the rule for a given data set. Having their origin in market basked analysis,association rules are now one of the most popular tools in data mining.This popularity is to a large part due to the availability of efficient algorithms following from the development of the Apriori algorithm.
We will review the basic Apriori algorithm and discuss variants for distributed data,inclusion of constraints and data taxonomies.The review ends with an outlook on tools which have the potential to deal with long itemsets and considerably reduce the amount of (uninteresting)itemsets returned.The discussion will focus on the problem of finding frequent itemsets.
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Hegland, M. (2003). Algorithms for Association Rules. In: Mendelson, S., Smola, A.J. (eds) Advanced Lectures on Machine Learning. Lecture Notes in Computer Science(), vol 2600. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36434-X_7
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DOI: https://doi.org/10.1007/3-540-36434-X_7
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