Data Mining in SQL Server Analysis Services

Explore the capabilities of SQL Server Analysis Services (SSAS) for data mining. Discover how to uncover hidden patterns, predict future trends, and gain valuable insights from your data using a variety of sophisticated algorithms.

Key Concepts

Data mining in SSAS involves several core components that work together to transform raw data into actionable knowledge:

• Mining Structures: The foundation for data mining. They define the data sources, relevant columns, and how the data will be processed for mining.
• Mining Models: The result of applying a data mining algorithm to a mining structure. Each model learns patterns from the data and can be used for predictions.
• Algorithms: The mathematical engines that analyze data to discover patterns. SSAS supports various algorithms, including:
  
  • Classification (e.g., Naive Bayes, Logistic Regression)
  • Clustering (e.g., K-Means)
  • Association Rules (e.g., Association Rules)
  • Sequence Analysis (e.g., Sequence Clustering)
  • Forecasting (e.g., ARIMA, Linear Regression)

Getting Started

Begin your journey into data mining with these essential steps:

1. Understand Your Data: Identify the business problem and the data required to solve it.
2. Create a Mining Structure: Define your data source, select relevant columns, and specify how data will be partitioned.
3. Train a Mining Model: Choose an appropriate algorithm and train a model using the mining structure.
4. Explore and Validate: Analyze the model's results, assess its accuracy, and refine it as needed.
5. Make Predictions: Use the trained model to predict outcomes for new data.

Common Data Mining Tasks

Reference and API