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What are association rules

Association rules are a powerful machine learning tool that allow to find oriented relations between a set of one or more objects and another set of objects in a large dataset. They are frequently applied when studying consumer baskets to find links between associated products.

In 1994, Rakesh Agrawal and Ramakrishnan Sikrant have proposed the Apriori algorithm to identify associations between items in the form of rules. This algorithm is used when the volume of data to be analyzed is important. As the number of items can be several tens of thousands, combinatorics is such that all the rules can not be studied. It is therefore necessary to limit the search for rules to the most important ones. The quality measurements are probabilistic values which limit the combinatorial explosion during the two phases of the algorithm, and allow the sorting of the results

XLSTAT allows you to apply this algorithm on Excel files but also on flat files.