Mining Models

Microsoft SQL Server Analysis Services provides robust tools and algorithms for building and managing data mining models. This section delves into the specifics of mining models, their components, and how they are created and utilized within Analysis Services.

What are Mining Models?

A mining model is the result of applying a data mining algorithm to a mining structure. It represents the patterns and insights discovered within your data. Each algorithm extracts different types of patterns. For example, a clustering algorithm will produce a model that groups similar data points, while a sequence clustering algorithm will identify common sequences of events.

Mining models are the core of the data mining process in Analysis Services. They are built upon a mining structure, which defines the data source and the way data is prepared for mining. The algorithm then uses this structure to train the model.

Components of a Mining Model

While the specific content varies depending on the algorithm used, most mining models in Analysis Services share a common structure:

• Algorithm: The specific data mining algorithm that was applied (e.g., Linear Regression, Decision Trees, Clustering).
• Parameters: The configuration settings and tuning options used when training the model.
• Content: The actual discovered patterns, rules, or relationships. This can include things like decision trees, association rules, regression formulas, or cluster centroids.
• Metadata: Information about the model, such as its name, creation date, and the mining structure it's based on.

Creating Mining Models

Mining models are created by training them against a mining structure. This process is typically done using SQL Server Data Tools (SSDT) or by scripting DMX (Data Mining Extensions) commands. The key steps involved are:

1. Define a Mining Structure: Specify the data source, select relevant columns, and define how the data should be processed (e.g., discretization of numeric columns, handling of missing values).
2. Select an Algorithm: Choose the appropriate data mining algorithm for your task.
3. Train the Model: Apply the chosen algorithm to the mining structure. Analysis Services processes the data and builds the model based on the patterns it finds.
4. Refine and Test: Evaluate the model's performance and accuracy. You may need to adjust algorithm parameters or refine the mining structure for better results.

Mining Model Viewer

Once a mining model is created, you can explore its contents using the Mining Model Viewer in SQL Server Management Studio (SSMS) or SQL Server Data Tools. The viewer provides a graphical interface to visualize the patterns discovered by the model. Different algorithms have specific viewers tailored to their output:

• Decision Tree Viewer: For visualizing decision trees.
• Association Rules Viewer: For viewing relationships between items.
• Clustering Viewer: For exploring data clusters.
• Linear Regression Viewer: For examining regression formulas.

These viewers are crucial for understanding the insights derived from your data and for validating the model's findings.

Discovering Patterns

The ultimate goal of creating mining models is to discover actionable insights from your data. These insights can be used for a variety of business purposes, such as:

• Customer segmentation and targeted marketing
• Predicting customer churn
• Identifying fraudulent transactions
• Optimizing product recommendations
• Forecasting sales trends

By leveraging the power of data mining in SQL Server Analysis Services, businesses can make more informed decisions and gain a competitive edge.