Specifies the minimum probability that a rule is true. For example, setting this value to 0.5 specifies that no rule with less than 50% probability is generated.
Specifies the method used to fill the gaps in historical data. By default, irregular gaps or ragged edges in data are not ...
Specifies the minimum dependency probability between input and output attributes. This value is used to limit the size of ...
Specifies the minimum number of cases that a leaf node must contain. Setting this value to less than 1 specifies the minimum ...
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 ...