Frequent item set mining and association rule induction [Agrawal et al. 1993, 1994] are powerful methods for so-called market basket analysis, which aims at finding regularities in the shopping behavior of customers of supermarkets, mail-order companies, online shops etc. With the induction of frequent item sets and association rules one tries to find sets of products that are frequently bought together, so that from the presence of certain products in a shopping cart one can infer (with a high probability) that certain other products are present. Such information, especially if expressed in the form of rules, can often be used to increase the number of items sold, for instance, by appropriately arranging the products on the shelves of a supermarket or on the pages of a mail-order catalog (they may, for example, be placed adjacent to each other in order to invite even more customers to buy them together) or by directly suggesting items to a customer, which may be of interest for him/her.
An association rule is a rule like "If a customer buys wine and bread, he/she often buys cheese, too." It expresses an association between (sets of) items, which may be products of a supermarket or a mail-order company, special equipment options of a car, optional services offered by telecommunication companies etc. An association rule states that if we pick a customer at random and find out that he/she selected certain items (bought certain products, chose certain options etc.), we can be confident, quantified by a percentage, that he/she also selected certain other items (bought certain other products, chose certain other options etc.).
A full description of the algorithm (included in the source package) is available on Christian Borgelts web page. The additional parameters described on this page can be applied via the additional parameter field of the "Advanced Settings" tab.
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