Association Rule Learner (Borgelt)

This rule learner* uses the Apriori (Agrawal et al. 1993) algorithm implemented by Christian Borgelt. The following description has been taken from his homepage.

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.

(*) RULE LEARNER is a registered trademark of Minitab, LLC and is used with Minitab’s permission.


Item column
The collection column that contains the item set to mine.
Minimum set size
The minimum size of a set. Note that both the Consequent and the Antecedent contribute to the set size, i.e. for a minimum set size of 3, the minimum number of elements in the Antecedent is actually 2 because the Consequent is part of the item set.
Limit set size
Whether to limit the maximum size of a set. It is highly recommended to limit the set size in order to keep memory consumption to a minimum.
Maximum set size
The maximum size of a set. Note that both the Consequent and the Antecedent contribute to the set size, i.e. for a maximum set size of 3, the maximum number of elements in the Antecedent is 2 because the Consequent is part of the item set.
Minimum support
The minimum support. Note that the smaller this value, the more itemsets are considered by the algorithms. For some datasets this can cause a very high memory consumption. If you find yourself in such a scenario, it is advised to increase the minimum support or at least limit the set size.
Minimum rule confidence
The minimum confidence of a association rule.
Sort antecedent list
The items in the antecedent list are sorted in ascending order if this option is selected.

Advanced Settings

Additional parameter
Additional parameter that should be passed to the algorithm separated by a space. For details about the available parameters see the detailed description of the apriori algorithm on Christian Borgelts web page.

Input Ports

Transaction list

Output Ports

Association Rules


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