Export (0) Print
Expand All
This topic has not yet been rated - Rate this topic

Predicting Associations (Intermediate Data Mining Tutorial)

After the models have been processed, you can use the information about associations stored in the model to create predictions. In the final task of this lesson, you learn how to build prediction queries against the association models that you created. This lesson assumes that you are familiar with how to use the Prediction Query Builder and want to learn how to build prediction queries against association models. For more information how to use Prediction Query Builder, see Creating DMX Prediction Queries.

Associative prediction can serve many purposes, such as recommending items to a customer, or finding relationships amongst products. To build a prediction query, you first select the association model you want to use, and then you specify the input data. Inputs can come from an external data source, such as a list of values, or you can build a singleton query and provide values as you go.

For this scenario, you will first create some singleton prediction queries, to get an idea of how prediction works. Then you will create a query for batch predictions that you could use for making recommendations based on a customer's current purchases.

To create a prediction query on an association model

  1. Click the Mining Model Prediction tab of Data Mining Designer.

  2. In the Mining Model pane, click Select Model. (You can skip this step and the next step if the correct model is already selected.)

  3. In the Select Mining Model dialog box, expand the node that represents the mining structure Association, and select the model Association. Click OK.

    For now, you can ignore the input pane.

  4. In the grid, click the empty cell under Source and select Prediction Function. In the cell under Field, select PredictAssociation.

    You can also use the Predict function to predict associations. If you do, be sure to choose the version of the Predict function that takes a table column as argument.

  5. In the Mining Model pane, select the nested table vAssocSeqLineItems, and drag it into the grid, to the Criteria/Argument box for the PredictAssociation function.

    Dragging and dropping table and column names lets you build complex statements without syntax errors. However, it replaces the current contents of the cell, which include other optional arguments for the PredictAssociation function. To view the other arguments, you can temporarily add a second instance of the function to the grid for reference.

  6. Click the Criteria/Argument box and type the following text after the table name: ,3

    The complete text in the Criteria/Argument box should be as follows:

    [Association].[v Assoc Seq Line Items],3

  7. Click the Results button in the upper corner of the Prediction Query Builder.

The expected results contain a single column with the heading Expression. The Expression column contains a nested table with a single column and the following three rows. Because you did not specify an input value, these predictions represent the most likely product associations for the model as a whole.

Model

Women's Mountain Shorts

Water Bottle

Touring-3000

Next, you will use the Singleton Query Input pane to specify a product as input to the query, and view the products that are most likely associated with that item.

To create a singleton prediction query with nested table inputs

  1. Click the Design button in the corner of the Prediction Query Builder to switch back to the query building grid.

  2. On the Mining Model menu, select Singleton Query.

  3. In the Mining Model dialog box, select the Association model.

  4. In the grid, click the empty cell under Source and select Prediction Function. In the cell under Field, select PredictAssociation.

  5. In the Mining Model pane, select the nested table vAssocSeqLineItems, and drag it into the grid, to the Criteria/Argument box for the PredictAssociation function. Type ,3 after the nested table name just as in the previous procedure.

  6. In the Singleton Query Input dialog box, click the Value box next to vAssoc Seq Line Items, and then click the (…) button.

  7. In the Nested Table Input dialog box, select Touring Tire in the Key column pane, and then click Add.

  8. Click the Results button.

The results now show the predictions for products that are most likely associated with the Touring Tire.

Model

Touring Tire Tube

Sport-100

Water Bottle

However, you already know from exploring the model that the Touring Tire Tube is frequently purchased with the Touring Tire; you are more interested in knowing what products you can recommend to customers who purchase these items together. You will change the query so that it predicts related products based on two items in the basket. You will also modify the query to add the probability for each predicted product.

To add inputs and probabilities to the singleton prediction query

  1. Click the Design button in the corner of the Prediction Query Builder to switch back to the query building grid.

  2. In the Singleton Query Input dialog box, click the Value box next to vAssoc Seq Line Items, and then click the (…) button.

  3. In the Key column pane, select Touring Tire, and then click Add.

  4. In the grid, click the empty cell under Source and select Prediction Function. In the cell under Field, select PredictAssociation.

  5. In the Mining Model pane, select the nested table vAssocSeqLineItems, and drag it into the grid, to the Criteria/Argument box for the PredictAssociation function. Type ,3 after the nested table name just as in the previous procedure.

  6. In the Nested Table Input dialog box, select Touring Tire Tube in the Key column pane, and then click Add.

  7. In the grid, in the row for the PredictAssociation function, click the Criteria/Argument box, and change the arguments to add the argument, INCLUDE_STATISTICS.

    The complete text in the Criteria/Argument box should be as follows:

    [Association].[v Assoc Seq Line Items], INCLUDE_STATISTICS, 3

  8. Click the Results button.

The results in the nested table now change to show the predictions, together with support and probability. For more information about how to interpret these values, see Mining Model Content for Association Models (Analysis Services - Data Mining).

Model

$SUPPORT

$PROBABILITY

$ADJUSTEDPROBABILITY

Sport-100

4334

0.291…

0.252…

Water Bottle

2866

0.192…

0.175…

Patch Kit

2113

0.142…

0.132

When there are many nested tables in the results, you might want to flatten the results for easier viewing. To do this, you can manually modify the query and add the FLATTENED keyword.

To flatten nested rowsets in a prediction query

  1. Click the SQL button in the corner of the Prediction Query Builder.

    The grid changes to an open pane where you can view and modify the DMX statement that was created by the Prediction Query Builder.

  2. After the SELECT keyword, type FLATTENED.

    The complete text of the query should be as follows:

    SELECT FLATTENED
      PredictAssociation([Association].[v Assoc Seq Line Items],INCLUDE_STATISTICS,3)
    FROM
      [Association]
    NATURAL PREDICTION JOIN
    (SELECT (SELECT 'Touring Tire' AS [Model]
      UNION SELECT 'Touring Tire Tube' AS [Model]) AS [v Assoc Seq Line Items]) AS t
    
  3. Click the Results button in the upper corner of the Prediction Query Builder.

Note that after you have manually edited a query, you will not be able to switch back to Design view without losing the changes. If you wish to save the query, you can copy the DMX statement that you created manually to a text file. When you change back to Design view, the query is reverted to the last version that was valid in Design view.

Suppose you want to know the best predictions for individual customers, based on past purchases. You can use external data as input to the prediction query, such as tables containing the customer ID and the most recent product purchases. The requirements are that the data tables be already defined as an Analysis Services data source view; moreover, the input data must contain case and nested tables like those used in the model. They need not have the same names, but the structure must be similar. For the purpose of this tutorial, you will use the original tables on which the model was trained.

To change the input method for the prediction query

  1. In the Mining Model menu, select Singleton Query again, to clear the check mark.

  2. An error message appears warning that your singleton query will be lost. Click Yes.

    The name of the input dialog box changes to Select Input Table(s).

Because you are interested in creating a prediction query that provides Customer ID and a list of products as input, you will add the customer table as the case table, and the purchases table as the nested table. Then you will add prediction functions to create recommendations.

To create a prediction query using nested table inputs

  1. In the Mining Model pane, select the Association Filtered model.

  2. In the Select Input Table(s) dialog box, click Select Case Table.

  3. In the Select Table dialog box, for Data Source, select AdventureWorksDW2008. In the Table/View Name list, select vAssocSeqOrders, and then click OK.

    The table vAssocSeqOrders is added to the pane.

  4. In the Select Input Table(s) dialog box, click Select Nested Table.

  5. In the Select Table dialog box, for Data Source, select AdventureWorksDW2008. In the Table/View name list, select vAssocSeqLineItems, and then click OK.

    The table vAssocSeqLineItems is added to the pane.

  6. In the Specify Nested Join dialog box, drag the OrderNumber field from the case table and drop it onto the OrderNumber field in the nested table.

    You can also click Add Relationship and create the relationship by selecting columns from a list.

  7. In the Specify Relationship dialog box, verify that the OrderNumber fields are mapped correctly, and then click OK.

  8. Click OK to close the Specify Nested Join dialog box.

    The case and nested tables are updated in the design pane to show the joins connecting the external data columns to the columns in the model. If the relationships are wrong, you can right-click the join line and select Modify Connections to edit the column mapping, or you can right-click the join line and select Delete to remove the relationship completely.

  9. Add a new row to the grid. For Source, select vAssocSeqOrders table. For Field, select CustomerKey.

  10. Add a new row to the grid. For Source, select vAssocSeqOrders table. For Field, select Region.

  11. Add a new row to the grid. For Source, select Prediction Function, and for Field, select PredictAssociation.

  12. Drag vAssocSeqLineItems, into the Criteria/Argument box of the PredictAssociation row. Click at the end of the Criteria/Argument box and then type the following text: INCLUDE_STATISTICS,3

    The complete text in the Criteria/Argument box should be: [Association].[v Assoc Seq Line Items], INCLUDE_STATISTICS, 3

  13. Click the Result button to view the predictions for each customer.

You might try creating a similar prediction query on the multiple models, to see whether filtering changes the prediction results. For more information about creating predictions and other types of queries, see Querying an Association Model (Analysis Services - Data Mining).

Did you find this helpful?
(1500 characters remaining)
Thank you for your feedback

Community Additions

ADD
Show:
© 2014 Microsoft. All rights reserved.