Association Rule Learner


Searches for frequent itemsets meeting the user-defined minimum support criterion and, optionally, creates association rules from them. The column containing the transactions (BitVectors or Collections) has to be selected. The minimum support as an absolute number must be provided (therefore check the number of transactions to obtain a sensible criterion). If the frequent itemsets should be free (unconstrained) or closed or maximal has also be defined. Closed itemsets are frequent itemsets, which have no superset with the same support, thus providing all the information from free itemsets in a compressed form. Maximal itemsets are sets which have no frequent superset at all. The maximal itemset length must also be defined. If association rules are generated, a confidence value has to be provided. The confidence is a value to define how often the rule is right. Association rules generated here are in the form to have only one item in the consequence. The underlying data structure used by the algorithm can be either an ARRAY or a TIDList. Choose the former when there are many transactions an less items, and the latter if the structure of the input data is vice versa.

Input Ports

  1. Type: Data Datatable containing transactions.

Output Ports

  1. Type: Data Datatable with discovered frequent itemsets or association rules.

Find here

Analytics > Mining > Item Sets / Association Rules

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