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Microsoft SQL Data Mining Algorithms

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The selection of data mining tools depends on the objectives of your data mining project. PCA uses the  Microsoft SQL Server Analytics Services – SASS – Data Mining tools built into the SQL Server Business Intelligence (BI) platform. This table describes, for each business use-case the relevant SQL Server Analytics Services – SSAS algorithm:

Business Purpose Description Algorithm(s)
Market Basket Analysis Discover items that are most frequently purchased together to optimize product bundles and placement. PCA does data mining with decision trees.
  • Association
  • Decision Trees
Churn Analysis Anticipate customers who may be considering canceling their service and target communication/services to improve customer retention. We do data mining with decision trees, linear regression and logistic regression.
  • Decision Trees
  • Linear Regression
  • Logistic Regression
Market Analysis Define market segments by automatically grouping similar customers together and use segmentation to target the most profitable customers. Clustering Algorithms are what everyone today sees in Market Basket analysis. For example, when Amazon shows you some products that you might like to buy along with the specific product you are looking at, they are using a clustering algorithm.
  • Clustering
  • Sequence Clustering
Sales Forecasting Predict sales and inventory amounts and learn how they are interrelated to foresee bottlenecks and improve performance
  • Decision Trees
  • Time Series
Data Exploration Analyze profitability across customers, or compare customers that prefer different brands of the same product to discover new opportunities
  • Neural Network
Unsupervised Learning Identify previously unknown relationships between various elements of your business to inform your decisions
  • Neural Network
Web Site Analysis Understand how people use your Web site and group similar usage patterns to offer a better experience
  • Sequence Clustering
Campaign Analysis Spend marketing funds more effectively by targeting the customers most likely to respond to a promotion
  • Decision Trees
  • Naïve Bayes
  • Clustering
Information Quality Identify and handle anomalies during data entry or data loading to improve the quality of information
  • Linear Regression
  • Logistic Regression
Text Analysis Analyze feedback to find common themes and trends that concern your customers or employees, informing decisions with unstructured input
  • Text Mining

Read more about Data Mining or contact us today for a Free Consultation regarding the best algorithms for your data mining project.