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Tutorials: Using DMX

After you have created a data mining solution by using Microsoft SQL Server Analysis Services, you can use Data Mining Extensions (DMX) to create queries against the data mining models. Queries can predict trends or retrieve patterns in the data. You can also use DMX to create and to manage mining structures and mining models.

The step-by-step tutorials in the following list demonstrate how to use DMX to build data mining models and run data mining queries.

These tutorials assume that you are already somewhat familiar with data mining concepts and are interested in learning how to use DMX to perform common data mining tasks.

  • Bike Buyer DMX Tutorial

    This tutorial walks you through the creation of a new mining structure and mining models by using the Data Mining Extensions (DMX) language, and explains how to create DMX prediction queries.

  • Market Basket DMX Tutorial

    This tutorial uses a typical market basket scenario, where you find associations between the products that customers purchase together. This tutorial also demonstrates how to use nested tables when you create a mining structure. You build and train a model based on this structure, and then create predictions using DMX.

  • Time Series Prediction DMX Tutorial

    This tutorial creates a forecasting model to illustrate the use of the CREATE MODEL (DMX) statement. You then add related models and customize the behavior of each by changing the parameters of the Microsoft Time Series algorithm. Finally you create predictions and update the predictions with new data. The ability to update a time series while making predictions is new in SQL Server 2008.

  • Basic Data Mining Tutorial

    This tutorial introduces basic concepts, such as how to create a project and how to build mining structures and mining models.

  • Intermediate Data Mining Tutorials (Analysis Services - Data Mining)

    This tutorial contains a number of independent lessons, each introducing you to a different model type. Each lesson walks you through the process of creating a model, exploring the model, and then customizing the model and creating prediction queries.

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