associationrulesnode properties

Association Rules node icon The Association Rules node is similar to the Apriori Node. However, unlike Apriori, the Association Rules node can process list data. In addition, the Association Rules node can be used with SPSS Analytic Server to process big data and take advantage of faster parallel processing.

Table 1. associationrulesnode properties
`associationrulesnode` properties	Data type	Property description
`predictions`	field	Fields in this list can only appear as a predictor of a rule
`conditions`	[field1...fieldN]	Fields in this list can only appear as a condition of a rule
`max_rule_conditions`	integer	The maximum number of conditions that can be included in a single rule. Minimum 1, maximum 9.
`max_rule_predictions`	integer	The maximum number of predictions that can be included in a single rule. Minimum 1, maximum 5.
`max_num_rules`	integer	The maximum number of rules that can be considered as part of rule building. Minimum 1, maximum 10,000.
`rule_criterion_top_n`	`Confidence` `Rulesupport` `Lift` `Conditionsupport` `Deployability`	The rule criterion that determines the value by which the top "N" rules in the model are chosen.
`true_flags`	Boolean	Setting as Y determines that only the true values for flag fields are considered during rule building.
`rule_criterion`	Boolean	Setting as Y determines that the rule criterion values are used for excluding rules during model building.
`min_confidence`	number	0.1 to 100 - the percentage value for the minimum required confidence level for a rule produced by the model. If the model produces a rule with a confidence level less than the value specified here the rule is discarded.
`min_rule_support`	number	0.1 to 100 - the percentage value for the minimum required rule support for a rule produced by the model. If the model produces a rule with a rule support level less than the specified value the rule is discarded.
`min_condition_support`	number	0.1 to 100 - the percentage value for the minimum required condition support for a rule produced by the model. If the model produces a rule with a condition support level less than the specified value the rule is discarded.
`min_lift`	integer	1 to 10 - represents the minimum required lift for a rule produced by the model. If the model produces a rule with a lift level less than the specified value the rule is discarded.
`exclude_rules`	Boolean	Used to select a list of related fields from which you do not want the model to create rules. Example: `set :gsarsnode.exclude_rules = [[[field1,field2, field3]],[[field4, field5]]]` - where each list of fields separated by [] is a row in the table.
`num_bins`	integer	Set the number of automatic bins that continuous fields are binned to. Minimum 2, maximum 10.
`max_list_length`	integer	Applies to any list fields for which the maximum length is not known. Elements in the list up until the number specified here are included in the model build; any further elements are discarded. Minimum 1, maximum 100.
`output_confidence`	Boolean
`output_rule_support`	Boolean
`output_lift`	Boolean
`output_condition_support`	Boolean
`output_deployability`	Boolean
`rules_to_display`	`upto` `all`	The maximum number of rules to display in the output tables.
`display_upto`	integer	If `upto` is set in `rules_to_display`, set the number of rules to display in the output tables. Minimum 1.
`field_transformations`	Boolean
`records_summary`	Boolean
`rule_statistics`	Boolean
`most_frequent_values`	Boolean
`most_frequent_fields`	Boolean
`word_cloud`	Boolean
`word_cloud_sort`	`Confidence` `Rulesupport` `Lift` `Conditionsupport` `Deployability`
`word_cloud_display`	integer	Minimum 1, maximum 20
`max_predictions`	integer	The maximum number of rules that can be applied to each input to the score.
`criterion`	`Confidence` `Rulesupport` `Lift` `Conditionsupport` `Deployability`	Select the measure used to determine the strength of rules.
`allow_repeats`	Boolean	Determine whether rules with the same prediction are included in the score.
`check_input`	`NoPredictions` `Predictions` `NoCheck`