Specifies the number of cases that the algorithm uses on each pass if the CLUSTERING_METHOD parameter is set to one of the scalable clustering methods. Setting SAMPLE_SIZE to 0 will cause the entire dataset to be clustered in a single pass, which can cause memory and performance issues.
Specifies the minimum number of cases that must contain the itemset before generating a rule. Setting this value to less ...
Specifies the minimum probability that a rule is true. For example, setting this value to 0.5 specifies that no rule with ...
Specifies the number of buckets in which to discretize (group) members of the attribute. The method used to discretize the ...
Specifies the number of cases that are used to train the model. The algorithm either uses the number specified by SAMPLE_SIZE ...
Specifies the number of cases that the algorithm uses on each pass if the CLUSTERING_METHOD parameter is set to one of the ...
Specifies the number of members in the attribute. This number is either the amount last counted by Analysis Services, or ...
Specifies the percentage of cases within the training data used to calculate the holdout error for this algorithm. HOLDOUT_PERCENTAGE ...
Specifies the position for binding within a collection (for example: the ordinal when binding to a KeyColumn of an attribute). ...
Specifies the prefix appended to any materialized views created for the cube's partitions. This setting will be used as the ...