Modifying and Processing the Market Basket Model (Intermediate Data Mining Tutorial)

 

Applies To: SQL Server 2016 Preview

Before you process the association mining model that you created, you must change the default values of two of the parameters: Support and Probability.

  • Support defines the percentage of cases in which a rule must exist before it is considered valid. You will specify that a rule must be found in at least 1 percent of cases.

  • Probability defines how likely an association must be before it is considered valid. You will consider any association with a probability of at least 10 percent.

For more information about the effects of increasing or decreasing support and probability, see Microsoft Association Algorithm Technical Reference.

After you have defined the structure and parameters for the Association mining model, you will process the model.

To adjust the parameters of the Association model

  1. Open the Mining Models tab of Data Mining Designer.

  2. Right-click the Association column in the grid in the designer and select Set Algorithm Parameters to open the Algorithm Parameters dialog box.

  3. In the Value column of the Algorithm Parameters dialog box, set the following parameters:

    MINIMUM_PROBABILITY = 0.1

    MINIMUM_SUPPORT = 0.01

  4. Click OK.

To process the mining model

  1. On the Mining Model menu of SQL Server Data Tools (SSDT), select Process Mining Structure and All Models.

  2. At the warning asking whether you want to build and deploy the project, click Yes.

    The Process Mining Structure - Association dialog box opens.

  3. Click Run.

    The Process Progress dialog box opens to display information about model processing. Processing of the new structure and model might take some time.

  4. After processing is complete, click Close to exit the Process Progress dialog box.

  5. Click Close again to exit the Process Mining Structure - Association dialog box.

Next Task in Lesson

Exploring the Market Basket Models (Intermediate Data Mining Tutorial)

See Also

Processing Requirements and Considerations (Data Mining)