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  | 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 | |
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Web Site Analysis  |
Understand how people use your Web site and group similar usage patterns to offer a better experience  |
1. Sequence Clustering | |
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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 | |
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Information Qualityensp; |
Identify and handle anomalies during data entry or data loading to improve the quality of information  |
1. Linear Regression 2. Logistic Regression | |
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Text Analysis  |
Analyze feedback to find common themes and trends that concern your customers or employees, informing decisions with unstructured input  |
1. Text Mining | |
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