Data Mining Algorithms in SQL Server Analysis Services

SQL Server Analysis Services (SSAS) provides a rich set of data mining algorithms that enable you to discover patterns, build predictive models, and gain insights from your data. These algorithms are integrated into the SSAS engine and can be accessed and managed using tools like SQL Server Management Studio (SSMS) and SQL Server Data Tools (SSDT).

Supported Data Mining Algorithms

SSAS offers the following core data mining algorithms:

Key Concepts in SSAS Data Mining

Data Mining Models

Data mining in SSAS involves creating predictive or descriptive models based on historical data. These models are stored within a SSAS database as mining structures, which contain the data source, training parameters, and the algorithm used.

Mining Structures and Mining Models

A mining structure defines the data you will use for data mining, including case tables, nested tables, and content columns. Associated with each mining structure are one or more mining models, each built using a specific algorithm.

Training and Prediction

The process of building a data mining model is called training. Once trained, the model can be used to make predictions on new, unseen data. SSAS provides tools to easily create and deploy these models.

Algorithm Parameters

Each algorithm has specific parameters that can be tuned to influence the model's performance and behavior. Understanding these parameters is crucial for effective data mining.

Getting Started with Data Mining

To start using data mining with SQL Server Analysis Services:

  1. Create a SSAS Database: Ensure you have a SQL Server Analysis Services instance and a database to work with.
  2. Define a Mining Structure: Use SQL Server Data Tools (SSDT) or SQL Server Management Studio (SSMS) to define your mining structure, connecting to your data source.
  3. Choose and Configure an Algorithm: Select the most appropriate algorithm for your business problem and configure its parameters.
  4. Train the Model: Process the mining structure to train the mining model.
  5. Explore and Predict: Use the mining viewer in SSMS to explore the trained model and generate predictions.

Example: Using Clustering to Segment Customers

Customer segmentation is a common use case for the clustering algorithm. By applying clustering to customer data, you can identify distinct groups of customers with similar purchasing behaviors or demographics. This insight can then be used to tailor marketing campaigns or product offerings.

-- Example T-SQL query to select data for mining
SELECT
    CustomerID,
    Age,
    Income,
    Region
FROM
    Sales.Customers
WHERE
    IsActive = 1;