Using Drillthrough on Structure Data (Basic Data Mining Tutorial)
Applies To: SQL Server 2016 Preview
As part of their advertising campaign, Adventure Works Cycles is sending a mailer to potential customers in the 34-40 age demographic. The marketing department has decided that they would also like to send the mailer to the customers who purchased bikes from Adventure Works Cycles more than five years ago. In this lesson you will identify customers with older bikes and retrieve their contact information. This information is not included in the model, but is included in the structure. To retrieve the contact information you will first ensure that drillthrough is enabled for the structure and then you will use drillthrough to reveal the names and addresses of the targeted customers.
To enable drillthrough on a mining model
In SQL Server Data Tools (SSDT), on the Mining Models tab of Data Mining Designer, right-click the TM_Decision_Tree model, and select Properties.
In the Properties windows, click AllowDrillthrough, and select True.
In the Mining Models tab, right-click the model, and select Process Model.
For more information, see Drillthrough Queries (Data Mining)
To view drillthrough data from a mining model
In Data Mining Designer, click the Mining Model Viewer tab.
Select the TM_Decision_Tree model from the Mining Model list.
Change the Background value to 1. By doing this, you show only the part of the model that is related to customer who bought bikes.
Select the Microsoft Tree viewer from the Viewer list. This will force the viewer to refresh with the new filter conditions. Then, locate the Age >=34 and <41 node and right-click the node.
Select Drill Through, and then select Model and Structure Columns to open the Drill Through window.
Scroll to the Structure.Date First Purchase column to view the purchase dates for the older bikes.
To copy the data to the Clipboard, right-click any row in the table, and select Copy All.
Congratulations, you have completed the basic data mining tutorial. Now that you are comfortable using the data mining tools, we recommend that you also complete the intermediate data mining tutorial, which demonstrates how to create models for forecasting, market basket analysis, and sequence clustering.