Data Mining Concepts in SQL Server Analysis Services

SQL Server Analysis Services (SSAS) provides powerful tools and functionalities for data mining, enabling you to discover patterns, predict future trends, and gain deeper insights from your data. This section explores the core concepts of data mining as implemented in SSAS.

What is Data Mining?

Data mining is the process of discovering meaningful patterns and relationships in large datasets. It involves using statistical algorithms, machine learning techniques, and database systems to analyze data and extract valuable information that can be used for decision-making, business intelligence, and predictive modeling.

Key Data Mining Tasks

SSAS supports several fundamental data mining tasks:

  • Classification

    Predicting a categorical outcome based on input variables. For example, predicting whether a customer will churn or not.

    Algorithms commonly used:

    • Decision Trees
    • Naive Bayes
    • Logistic Regression

  • Clustering

    Grouping similar data points together based on their characteristics. Useful for market segmentation or identifying customer groups.

    Algorithms commonly used:

    • K-Means

  • Regression

    Predicting a continuous numerical value. For example, predicting the sales revenue for the next quarter.

    Algorithms commonly used:

    • Linear Regression

  • Association Rules

    Discovering relationships between items in a dataset, often used in market basket analysis. For example, "Customers who buy bread also tend to buy milk."

    Algorithms commonly used:

    • Association Rules (Apriori)

  • Sequence Analysis

    Identifying patterns in data where the order of events is important. For example, analyzing the sequence of website visits leading to a purchase.

    Algorithms commonly used:

    • Sequence Clustering
    • Time Series Analysis

Core Components in SSAS Data Mining

SSAS data mining relies on several key components:

  • Data Sources: The datasets from which mining models are built. These can be relational databases, flat files, or other data sources accessible by SSAS.
  • Mining Structures: The schema that defines the data to be mined, including the input columns, predictable columns, and the training data.
  • Mining Models: The actual predictive models generated by applying data mining algorithms to the mining structure.
  • Algorithms: The various statistical and machine learning algorithms that perform the mining tasks.
  • Mining Dimensions: Columns in the data that are used as input or as predictable targets for the mining models.
  • Scenarios: Different configurations and applications of mining models for specific business questions.

Building and Using Data Mining Models

The process typically involves:

  1. Connecting to your data sources.
  2. Creating a Mining Structure, defining the data columns and their roles.
  3. Selecting and configuring data mining algorithms to create Mining Models.
  4. Training the models using your data.
  5. Exploring and validating the models to understand their performance and insights.
  6. Using the models for predictions or querying the discovered patterns.

Example: Customer Churn Prediction

Consider a telecommunications company wanting to predict which customers are likely to switch to a competitor (churn). SSAS can be used to build a classification model:

SELECT
T.customer_key,
PREDICT_PROBABILITY(dt_model, 1) AS ChurnProbability
FROM
[MyMiningModel]
NATURAL PREDICTION JOIN
[myDataSourceView].vTargetMail AS T
WHERE
PREDICT_NODE_ID(dt_model) <> '0'

This query would use a trained decision tree model (`dt_model`) to predict the probability of churn for each customer based on their attributes.

Data mining requires careful planning, data preparation, and interpretation. Understanding your business objectives and the nature of your data is crucial for successful data mining implementation.

Further Reading