SQL Server Analysis Services Data Mining Tutorial

Learn to build and deploy data mining solutions using Analysis Services.

Introduction to Data Mining with SSAS

This tutorial will guide you through the essential steps of creating a data mining model using Microsoft SQL Server Analysis Services (SSAS). We will cover data preparation, model creation, testing, and deployment.

Why Data Mining?

Data mining is the process of discovering patterns and insights from large datasets. SSAS provides a powerful platform for building various types of data mining models, including:

  • Classification
  • Clustering
  • Association Rules
  • Sequence Analysis
  • Forecasting

Prerequisites

Before you begin, ensure you have the following installed:

  • SQL Server 2022 (or a compatible version)
  • SQL Server Data Tools (SSDT) for Visual Studio
  • Sample databases (e.g., AdventureWorksDW)

Step 1: Setting Up Your Project

We'll start by creating a new Analysis Services project in Visual Studio.

  1. Open SQL Server Data Tools (SSDT) for Visual Studio.
  2. Go to File > New > Project...
  3. In the project templates, navigate to Business Intelligence > Analysis Services.
  4. Select Analysis Services Project and click OK.
  5. Name your project (e.g., `SalesDataMiningTutorial`).
  6. In the Analysis Services Project Wizard, choose to create a new data source.
  7. Configure your data source to connect to your SQL Server instance and the appropriate data warehouse database (e.g., `AdventureWorksDW`).
  8. Create a new dimension later if needed, or use existing dimensions from your data warehouse.
  9. Define a cube or a tabular model as the basis for your data mining. For this tutorial, we'll assume a relational data source is sufficient.

Configuring the Data Source View

The Data Source View (DSV) is a crucial component that defines the scope of data available for mining. It allows you to join tables, rename columns, and create calculated columns.

Data Source View Example

Step 2: Creating a Data Mining Model

Now, let's create our first data mining model. We'll build a customer segmentation model using the clustering algorithm.

  1. Right-click on the Mining Structures folder in Solution Explorer and select New Mining Structure....
  2. In the Mining Structure Wizard, select Create a mining structure from an existing source.
  3. Choose your data source and data source view.
  4. Select the Clustering mining method.
  5. Drag and drop relevant columns from your data source view into the structure definition. For customer segmentation, consider columns like age, income, purchase history, etc.
  6. ● For example, include `Age`, `YearlyIncome`, `TotalChildren`, `NumCarsOwned`, and a customer key if available.
  7. Specify the Content for each column (e.g., `Numeric` for age and income, `Discretized` for age groups if preferred).
  8. Set the Usage for each column (e.g., `Predict` for the clustering outcome, `Input` for the features).
  9. Finish the wizard.

Clustering Algorithm

The clustering algorithm aims to group similar data points together without prior knowledge of the groups. It identifies natural groupings within your data.

Step 3: Training and Browsing the Model

Once the structure is defined, we need to train the model with our data and then explore the results.

  1. Right-click on your newly created Mining Structure in Solution Explorer and select Process....
  2. Confirm the processing options and click Run. Wait for the process to complete successfully.
  3. Right-click on the Mining Structure again and select Browse....
  4. The Mining Model Viewer will open. Select the Clustering Viewer.
  5. You will see clusters represented visually. Explore the characteristics of each cluster by clicking on them.
  6. The viewer shows the distribution of attributes within each cluster, helping you understand what defines each group.

Example Cluster Insights:

Cluster 0 might represent younger, budget-conscious customers with fewer purchases. Cluster 1 could be affluent families with multiple cars and higher incomes.
Clustering Viewer Example

Step 4: Testing and Validation

It's important to validate the quality and usefulness of your data mining model.

Using the Mining Accuracy Chart

For predictive models, you can use the Mining Accuracy Chart to assess performance. While less direct for clustering, you can evaluate cluster distinctiveness and predictability.

  1. In the Browse... view, switch to the Mining Accuracy Chart tab.
  2. Select your model and a predictable attribute (if applicable for testing).
  3. Analyze the chart to understand the model's accuracy (e.g., precision, recall).

Interpreting Cluster Quality

For clustering, assess the interpretability of the clusters. Do they make business sense? Are they distinct enough? You can further refine your model by adjusting input columns, discretization settings, or algorithm parameters.

Step 5: Deploying the Model

Once satisfied, you can deploy your SSAS project to a server.

  1. In Solution Explorer, right-click on your Analysis Services project and select Properties.
  2. Under Configuration Properties > Development, set the Server property to your SSAS instance name.
  3. Right-click on the project again and select Deploy.
  4. The deployed model can then be queried by applications or integrated into reporting solutions (e.g., SSRS, Power BI).

Querying Your Model

You can query your data mining models using the DISCOVER keyword or MDX with data mining extensions.

SELECT
    FLATTENED DISTINCT SHAPE.*
FROM
    [SalesDataMining]
    -- Replace 'Customer Segmentation' with your actual mining model name
    -- and 'Customer Clustering' with your actual mining table name.
    -- The parameters 'CLUSTER_COUNT' and 'CLUSTER_PROBABILITY_LOW'
    -- are specific to the clustering algorithm.
    -- Refer to SSAS documentation for detailed DISCOVER syntax.
    -- Example for DISCOVER:
    -- CALL System.Microsoft.DataMining.SqlServer.Discover(
    --     'Customer Segmentation',
    --     'Customer Clustering',
    --     '[Customer]', -- Specify the case table
    --     'Cluster' -- Specify the mining function
    -- )
    -- AS DiscoverResult

    -- Example using MDX (less common for direct model queries, more for model usage)
    -- SELECT
    --     [Measures].[SomeMeasure] ON COLUMNS,
    --     [DimCustomer].[CustomerKey].MEMBERS ON ROWS
    -- FROM [YourCube]
    -- WHERE [DimCustomer].[CustomerKey].CurrentMember.DM_PREDICT_CLUSTER( CLUSTER( [Customer Segmentation] ) )

Conclusion

Congratulations! You have successfully created, trained, and explored a data mining model using SQL Server Analysis Services. This tutorial provides a foundation; explore other algorithms and advanced features to unlock deeper insights from your data.

Continue learning by exploring:

  • Association Rules for market basket analysis.
  • Decision Trees for classification problems.
  • Time Series for forecasting.
  • Feature engineering and advanced model tuning.

For more detailed information, refer to the official Microsoft SQL Server Analysis Services documentation.