Microsoft SQL Data Mining Algorithms

The following table describes the different business use-cases that drive data mining adoption, and the relevant SQL Server Analytics Services (SSAS) algorithms:

Business Purpose 
Description 
 Algorithms(s)

Market Basket
Analysis 

Discover items that are most frequently purchased together to optimize product
bundles and placement 

1. Association
2. Decision Trees

Churn Analysis 

Anticipate customers who may be considering canceling their service and target communication/services to improve customer retention 

1. Decision Trees
2. Linear Regression
3. Logistic Regression

Market Analysis 

Define market segments by automatically grouping similar customers together and use segmentation to target the most profitable customers 

1. Clustering
2. Sequence Clustering

Sales Forecasting 

Predict sales and inventory amounts and learn how they are interrelated to foresee bottlenecks and improve performance 

1. Decision Trees
2. Time Series

Data Exploration 

Analyze profitability across customers, or compare customers that prefer different brands of the same product to discover new opportunities 

1. Neural Network

Unsupervised Learning 

Identify previously unknown relationships between various elements of your business to inform your decisions 

1. Neural Network

Web Site Analysis 

Understand how people use your Web site and group similar usage patterns to offer a better experience 

1. Sequence Clustering

Campaign Analysis 

Spend marketing funds more effectively by targeting the customers most likely to respond to a promotion 

1. Decision Trees
2. Naïve Bayes
3. Clustering

Information Qualityensp;

Identify and handle anomalies during data entry or data loading to improve the quality of information 

1. Linear Regression
2. Logistic Regression

Text Analysis 

Analyze feedback to find common themes and trends that concern your customers or employees, informing decisions with unstructured input 

1. Text Mining