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Understanding customer buying patterns is of great interest to the retailindustry and has shown to benefit a wide variety of goals ranging from managingstocks to implementing loyalty programs. Association rule mining is a commontechnique for extracting correlations such as "people in the South of Francebuy ros\'e wine" or "customers who buy pat\'e also buy salted butter and sourbread." Unfortunately, sifting through a high number of buying patterns is notuseful in practice, because of the predominance of popular products in the toprules. As a result, a number of "interestingness" measures (over 30) have beenproposed to rank rules. However, there is no agreement on which measures aremore appropriate for retail data. Moreover, since pattern mining algorithmsoutput thousands of association rules for each product, the ability for ananalyst to rely on ranking measures to identify the most interesting ones iscrucial. In this paper, we develop CAPA (Comparative Analysis of PAtterns), aframework that provides analysts with the ability to compare the outcome ofinterestingness measures applied to buying patterns in the retail industry. Wereport on how we used CAPA to compare 34 measures applied to over 1,800 storesof Intermarch\'e, one of the largest food retailers in France.

Testing Interestingness Measures in Practice: A Large-Scale Analysis of Buying Patterns