Data Mining Concepts in SQL Server Analysis Services

Last updated: October 26, 2023

This document provides an overview of fundamental data mining concepts as they are implemented within Microsoft SQL Server Analysis Services (SSAS). Data mining is the process of discovering patterns and relationships in large datasets. SSAS offers a robust set of tools and algorithms to facilitate this process.

Key Data Mining Concepts

1. Data Mining Goals

Before embarking on a data mining project, it's crucial to define the objective. Common data mining goals include:

• Prediction: Forecasting future values or behaviors based on historical data (e.g., predicting customer churn).
• Association: Discovering relationships between items in a dataset (e.g., market basket analysis, "customers who bought X also bought Y").
• Clustering: Grouping similar data points together without prior knowledge of the groups (e.g., segmenting customers into distinct profiles).
• Classification: Assigning data points to predefined categories (e.g., classifying emails as spam or not spam).
• Anomaly Detection: Identifying unusual data points that deviate significantly from the norm (e.g., detecting fraudulent transactions).

2. Data Mining Algorithms

SQL Server Analysis Services supports a variety of algorithms, each suited for different data mining tasks:

• Association Rules (Microsoft: Association Rules): Identifies items that frequently occur together.
• Clustering (Microsoft: Clustering): Segments data into distinct groups.
• Classification (Microsoft: Decision Trees, Microsoft: Naive Bayes, Microsoft: Logistic Regression): Predicts categorical outcomes.
• Regression (Microsoft: Linear Regression): Predicts numerical outcomes.
• Sequence Clustering (Microsoft: Sequence Clustering): Identifies patterns in sequential data.
• Time Series Forecasting (Microsoft: Time Series): Predicts future values in a time-ordered sequence.

3. Data Mining Models

A data mining model is the output of running a data mining algorithm against a dataset. It represents the patterns and knowledge discovered. In SSAS, models are stored within a data mining schema and can be queried using DMX (Data Mining Extensions).

4. Mining Structures

A mining structure is the container for the data used to create one or more data mining models. It defines the data sources, the columns to be used, and how they are processed (e.g., discretization of numerical data). A single mining structure can contain multiple models built using different algorithms on the same data.

5. Data Mining Dimensions and Measures

Data mining in SSAS often leverages the dimensional model (cubes, dimensions, and measures) already present in your Analysis Services project. This allows you to mine data directly from your cubes, making it easier to integrate insights back into your business intelligence solutions.

• Dimensions: Provide context for the data. Attributes within dimensions can be used as input or prediction targets in mining models.
• Measures: Numerical values that can be aggregated. While not typically direct input for predictive models, measures can be analyzed for trends and patterns.

6. Data Mining Extensions (DMX)

DMX is a query language used to interact with data mining models in SQL Server Analysis Services. It allows you to:

• Create and manage mining structures and models.
• Select data for training models.
• Query models to retrieve predictions, associations, cluster characteristics, and more.

For example, predicting a customer's likelihood to churn might involve a DMX query like:

7. Data Mining Visualization

SSAS provides graphical tools within SQL Server Management Studio (SSMS) and SQL Server Data Tools (SSDT) to visualize data mining models. This helps in understanding complex patterns discovered by the algorithms, such as decision trees, cluster characteristics, and association rules.

Getting Started with Data Mining in SSAS

To implement data mining in your SSAS project:

1. Design your multidimensional or tabular model (if not already done).
2. Create a Data Mining Project in SSDT.
3. Define a Mining Structure, selecting your data source(s) and relevant columns.
4. Choose and configure appropriate Data Mining Algorithms.
5. Train the mining models.
6. Explore and visualize the results.
7. Query the models using DMX for predictions and insights.

By understanding these core concepts, you can effectively leverage the powerful data mining capabilities of SQL Server Analysis Services to uncover valuable insights from your data.