Creating Mining Models

This document provides a comprehensive guide to creating mining models in SQL Server Analysis Services (SSAS). Mining models are the core components of data mining solutions, enabling you to discover patterns and make predictions from your data.

Steps to Create a Mining Model

Creating a mining model involves several key steps:

  1. Define the Data Source: Select and configure your data source view to specify the data that will be used for mining.
  2. Choose a Mining Algorithm: Select an appropriate algorithm based on your business problem and data characteristics. Common algorithms include:
    • Clustering (K-Means, BIRCH)
    • Classification (Naïve Bayes, Decision Trees, Logistic Regression)
    • Regression (Linear Regression)
    • Association Rules
    • Sequence Clustering
  3. Select Input and Predictor Columns: Identify the columns that will be used as input for the algorithm and the columns you want to predict.
  4. Configure Algorithm Parameters: Adjust algorithm-specific parameters to fine-tune the model's performance.
  5. Train the Model: Process the data to build the mining model.
  6. Test and Validate: Evaluate the model's accuracy and relevance using various metrics and techniques.

Using SQL Server Data Tools (SSDT)

The most common way to create mining models is by using SQL Server Data Tools (SSDT) in Visual Studio. SSDT provides a rich graphical interface for defining and managing your data mining projects.

Creating a New Mining Model Project:

  1. Open Visual Studio and select File > New > Project.
  2. Under Business Intelligence, select Analysis Services Projects.
  3. Enter a project name and location, then click OK.

Adding a Mining Model to a Cube or Dimension:

  1. In Solution Explorer, right-click on the Mining Models folder within your Analysis Services project.
  2. Select New Mining Model.
  3. Follow the steps in the Mining Model Wizard to select your data source, algorithm, and columns.

Example: Creating a Clustering Model

Let's create a simple clustering model to segment customers based on their purchasing behavior.

-- Example of selecting data for a mining model
SELECT
    CustomerID,
    TotalSpend,
    NumberOfPurchases,
    AverageItemPrice
FROM
    CustomerData;

Using the Mining Model Wizard for Clustering:

  • Choose the Basic Clustering algorithm.
  • Use TotalSpend, NumberOfPurchases, and AverageItemPrice as input columns.
  • No predictor columns are typically needed for pure clustering.
  • Specify the desired number of clusters or let the algorithm determine it.

Advanced Concepts

  • Drillthrough: Enable drillthrough to view the original data records that contributed to specific patterns.
  • Model Properties: Configure various properties for your mining model, such as the content type and maximum number of cases.
  • Parameter Tuning: Experiment with algorithm parameters to optimize prediction accuracy and pattern discovery.

For more detailed information on specific algorithms and advanced configurations, please refer to the related documentation sections.

Next: Mining Model Content