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).

See Also

Mining Model Content for Association Models (Analysis Services - Data Mining) PredictAssociation (DMX)

How to: Create a Prediction Query

Lesson 4: Building a Sequence Clustering Scenario (Intermediate Data Mining Tutorial)

The marketing department of Adventure Works Cycles wants to understand how customers move through the Adventure Works Cycles Web site. The company suspects that there is a pattern to the order in which customers put products into their shopping baskets. They want to analyze the order of purchase sequences to learn how customers add related items to their baskets. They can then use this information to streamline the flow of the Web site so that it leads customers to purchase additional products.

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After you complete the tasks in this lesson, you will have created a mining model that uses the Microsoft Sequence Clustering algorithm to predict the next item that customers will put into their shopping baskets. You will experiment with two versions of the model: one that analyzes only the order of products in the basket, and one that contains some additional customer demographics for clustering. Finally, you will use the models to create predictions that you can use to recommend products to customers.

To complete the tasks in the lesson, you will use the market basket mining structure that you created in Lesson 3: Building a Market Basket Scenario (Intermediate Data Mining Tutorial). This lesson contains the following tasks:

•Creating a Sequence Clustering Mining Model Structure

•Processing the Sequence Clustering Model

•Exploring the Sequence Clustering Models

•Creating and Modifying a Related Sequence Clustering Model (Intermediate Data Mining Tutorial)

•Creating Predictions on a Sequence Clustering Model (Intermediate Data Mining Tutorial)

Next Task in Lesson

Creating a Sequence Clustering Mining Model Structure

All Lessons

Lesson 1: Creating the Intermediate Data Mining Solution

Lesson 2: Forecasting Scenario (Intermediate Data Mining Tutorial) Lesson 3: Market Basket Scenario (Intermediate Data Mining Tutorial)

Lesson 4: Sequence Clustering Scenario (Intermediate Data Mining Tutorial)

Lesson 5: Neural Network and Logistic Regression Scenario (Intermediate Data Mining Tutorial)

See Also

Data Mining Tutorial

Intermediate Data Mining Tutorial (Analysis Services - Data Mining)

Creating a Sequence Clustering Mining Model Structure (Intermediate Data Mining Tutorial)

The first step in creating a sequence clustering mining model is to use the Data Mining Wizard to create a new mining structure and a mining model based on the Microsoft Sequence Clustering algorithm.

You will use the same data source view that you used for the market basket analysis, but you will add a column that contains the sequence identifier. In this scenario, the sequence means the order in which the customer added items to the shopping basket.

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You will also add some columns that are used in one of the models to group customers by demographics.

Procedures

To create a sequence clustering structure and model

1.In Solution Explorer in SQL Server Data Tools (SSDT), right-click Mining Structures and select New Mining Structure.

2.On the Welcome to the Data Mining Wizard page, click Next.

3.On the Select the Definition Method page, verify that From existing relational database or data warehouse is selected, and then click Next.

4.On the Create the Data Mining Structure page, verify that the option Create mining structure with a mining model is selected. Next, click the dropdown list for the option, Which data mining technique do you want to use?, and select Microsoft Sequence Clustering. Click Next.

The Select Data Source View page appears. Under Available data source views, select Orders.

Orders is the same data source view that you used for the market basket scenario. If you have not created this data source view, see Adding a Data Source View with Nested Tables (Intermediate Data Mining Tutorial).

5.Click Next.

6.On the Specify Table Types page, select the Case check box next to the vAssocSeqOrders table, and select the Nested check box next to the vAssocSeqLineItems table. Click Next.

nNote

If an error occurs when you select the Case or Nested check boxes, it may be that the join in the data source view is not correct. The nested table, vAssocSeqLineItems, must be connected to the case table, vAssocSeqOrders, by a many-to-one join. You can edit the relationship

by right-clicking on the join line and then reversing the direction of the join. For more information, see Create/Edit Relationship Dialog Box (Analysis Services - Multidimensional Data).

7.On the Specify the Training Data page, choose the columns for use in the model by selecting a check box as follows:

•IncomeGroup Select the Input check box.

This column contains interesting information about the customers that you can use for clustering. You will use it in the first model and then ignore it in the second model.

•OrderNumber Select the Key check box.

This field will be used as the identifier for the case table, or Key. In general,

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you should never use the key field of the case table as an input, because the key contains unique values that are not useful for clustering.

•Region Select the Input check box.

This column contains interesting information about the customers that you can use for clustering. You will use it in the first model and then ignore it in the second model.

•LineNumber Select the Key and Input check boxes.

The LineNumber field will be used as the identifier for the nested table, or Sequence Key. The key for a nested table must always be used for input.

•Model Select the Input and Predictable check boxes.

Verify that the selections are correct, and then click Next.

8.On the Specify Columns' Content and Data Type page, verify that the grid contains the columns, content types, and data types shown in the following table, and then click Next.

9.On the Create Testing Set page, change the Percentage of data for testing to 20, and then click Next.

10.On the Completing the Wizard page, for the Mining structure name, type Sequence Clustering with Region.

11.For the Mining model name, type Sequence Clustering with Region.

12.Check the Allow drill through box, and then click Finish.

Next Task in Lesson

Processing the Sequence Clustering Model

See Also

Data Mining Designer

Microsoft Sequence Clustering Algorithm

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