Building Models The fourth step in the data mining process, as highlighted in the following diagram, is to build the mining model or models. You will use the knowledge that you gained in the Exploring Data step to help define and create the models.
You define the columns of data that you want to use by creating a mining structure. The mining structure is linked to the source of data, but does not actually contain any data until you process it. When you process the mining structure, Analysis Services generates aggregates and other statistical information that can be used for analysis. This information can be used by any mining model that is based on the structure. For more information about how mining structures are related to mining models, see Logical Architecture (Analysis Services - Data Mining).
Before the structure and model is processed, a data mining model too is just a container that specifies the columns used for input, the attribute that you are predicting, and parameters that tell the algorithm how to process the data. Processing a model is often called training. Training refers to the process of applying a specific mathematical algorithm to the data in the structure in order to extract patterns. The patterns that you find in the training process depend on the selection of training data, the algorithm you chose, and how you have configured the algorithm. SQL Server 2017 contains many different algorithms, each suited to a different type of task, and each creating a different type of model. For a list of the algorithms provided in SQL Server 2017, see Data Mining Algorithms (Analysis Services - Data Mining).
You can also use parameters to adjust each algorithm, and you can apply filters to the training data to use just a subset of the data, creating different results. After you pass data through the model, the mining model object contains summaries and patterns that can be queried or used for prediction.
You can define a new model by using the Data Mining Wizard in SQL Server Data Tools, or by using the Data Mining Extensions (DMX) language. For more information about how to use the Data Mining Wizard, see Data Mining Wizard (Analysis Services - Data Mining). For more information about how to use DMX, see Data Mining Extensions (DMX) Reference.
It is important to remember that whenever the data changes, you must update both the mining structure and the mining model. When you update a mining structure by reprocessing it, Analysis Services retrieves data from the source, including any new data if the source is dynamically updated, and repopulates the mining structure. If you have models that are based on the structure, you can choose to update the models that are based on the structure, which means they are retrained on the new data, or you can leave the models as is. For more information, see Processing Requirements and Considerations (Data Mining).