For example, the following rule,
beer <= cannedveg & frozenmeal (173, 17.0%, 0.84), states that beer often occurs when cannedveg and frozenmeal occur together. The rule is 84% reliable and applies to 17% of the data, or 173 records.
Association rule algorithms automatically find the associations that you could find manually using visualization techniques, such as a Web diagram.
The Apriori node is one of two nodes covered in the Association Rules node. Apriori extracts a set of rules from the data, pulling out the rules with the highest information content. Apriori offers five different methods of selecting rules and uses a sophisticated indexing scheme to process large data sets efficiently. For large problems, Apriori is generally fast to train; it has no arbitrary limit on the number of rules that can be retained, and it can handle rules with up to 32 preconditions. Apriori requires that input and output fields all be categorical but delivers better performance because it is optimized for this type of data.
Like your visualization? Why not deploy it? For more information, see Deploy a model.