Association Rule Learner (deprecated)

This Node Is Deprecated — This node is kept for backwards-compatibility, but the usage in new workflows is no longer recommended. The documentation below might contain more information.

The 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 bitvectors 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 itemsets which have no frequent superset at all. The maximal itemset length must also be defined. If only frequent itemset are mined (and no association rules generated) the output can be sorted by support of the itemsets or their length. 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 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.

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


Column containing bitvectors
Select the column containing the bitvectors to mine for frequent itemsets or association rules. There ust be at least one, since this is the only valid input for the subgroup miner.
Minimum Support
An itemset is considered to be frequent if there are at least "minimum support" transactions, where the itemset occurs. Make sure, to have here a meaningful number in proportion of the number of rows of the input.
Itemset type
Choose either free, closed or maximal. Free are mostly redundant, closed provide the most information and maximal may hide some information.
Maximal itemset length
The maximal length of the resulting itemsets. A lower value may reduce the runtime if there are very long frequent itemsets.
Sort output table
The sorting is only for the frequent itemsets available (not for the association rules). They can be sorted either by itemset length or by their support. Choose NONE, if no sorting should be done.
Output association rules
Check if association rules should be generated out of the frequent itemsets. Note: association rules are always generated from free frequent itemsets and are contrained to have only one item in the consequence.
The confidence is a measure for "how often the rule is right". Thus, how often, if the items in the antecedence appeared also the consequence occured in the transactions.
Underlying data structure
Either ARRAY or TIDList. Array is recommended when the number of transactions (rows) is larger than the numer of items, and the TIDList if the number of rows is small and the number of items large. In general, the array needs more memory and is a bit faster, whereas the TIDList need less memory but is a bit slower.

Input Ports

Datatable containing bitvectors.

Output Ports

Datatable with discovered frequent itemsets or association rules.

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