MSAS - Building a Relational Decision Tree Model

Let us assume FoodMart wants the members with Golden Membership to be studied. It wants to focus on broadening the membership of the Gold Card. This can be done now using the Decision Tree Model.

The Decision Tree model can be created from the relational data contained in the FoodMart 2000 Access database.

1. In the Analysis Manager tree pane right click the Mining model folder and select New mining model…

2. In the Wizard Welcome screen click Next to proceed.

3. In the Select the source type screen select ROLAP model.

4. Next select the source table for defining our database. The window displays the available data sources. New data sources can be added by clicking on the New data source button. We will build our decision tree by using two tables:--Customers and Sales_fact_1998. Select the radio button for Multiple tables. Now add the selected tables from the list of those available by double clicking on it or selecting it and clicking the arrows to move the tables.

5. In the next screen we have to choose the Algorithm for the mining model. Select Microsoft Decision Tree and click Next.

6. In the next screen we are prompted to define how the tables are related. We can edit and create joins on this screen. A default join is displayed and this can be changed if we are not satisfied.

7. In the next screen we will proceed to define the Key column that will uniquely identify our case. Since our focus is on customer we will select the customer ID as the column key for analysis.

8. Next we will proceed to identify input and prediction columns. Input columns are considered during the partitioning process and give the user the best split. The aim is to identify the demographic factors that determine Golden card holder’s behavior. Let us add member_card from customer table and unit_sales from the Sales fact 1998 table to the predictable columns and all the columns from the customer table to the Input Columns pane.