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docs/2014/analysis-services/data-mining/create-drillthrough-queries-using-dmx.md

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 # Create Drillthrough Queries using DMX
   For all models that support drillthrough, you can retrieve case data and structure data by creating a DMX query in [!INCLUDE[ssManStudioFull](../../includes/ssmanstudiofull-md.md)] or any other client that supports DMX.  

docs/2014/analysis-services/data-mining/cross-validation-analysis-services-data-mining.md

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 # Cross-Validation (Analysis Services - Data Mining)
   *Cross-validation* is a standard tool in analytics and is an important feature for helping you develop and fine-tune data mining models. You use cross-validation after you have created a mining structure and related mining models to ascertain the validity of the model.  Cross-validation has the following applications:  

docs/2014/analysis-services/data-mining/cross-validation-formulas.md

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 # Cross-Validation Formulas
   When you generate a cross-validation report, it contains accuracy measures for each model, depending on the type of mining model (that is, the algorithm that was used to create the model), the data type of the predictable attribute, and the predictable attribute value, if any.  

docs/2014/analysis-services/data-mining/customize-mining-models-and-structure.md

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 # Customize Mining Models and Structure
   After you have selected an algorithm that meets your business needs, you can customize the mining model in the following ways to potentially improve results.

docs/2014/analysis-services/data-mining/data-definition-queries-data-mining.md

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 # Data Definition Queries (Data Mining)
   For data mining, the category *data definition query* means DMX statements or XMLA commands that do the following:  

docs/2014/analysis-services/data-mining/data-mining-algorithms-analysis-services-data-mining.md

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 # Data Mining Algorithms (Analysis Services - Data Mining)
   A *data mining algorithm* is a set of heuristics and calculations that creates a data mining model from data. To create a model, the algorithm first analyzes the data you provide, looking for specific types of patterns or trends. The algorithm uses the results of this analysis to define the optimal parameters for creating the mining model. These parameters are then applied across the entire data set to extract actionable patterns and detailed statistics.  

docs/2014/analysis-services/data-mining/data-mining-architecture.md

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 # Data Mining Architecture
   This section describes the architecture of data mining solutions that are hosted in an instance of [!INCLUDE[ssASnoversion](../../includes/ssasnoversion-md.md)]. The topics in this section describe the logical and physical architecture of an [!INCLUDE[ssASnoversion](../../includes/ssasnoversion-md.md)] instance that supports data mining, and also provide information about the clients, providers, and protocols that can be used to communicate with data mining servers, and to work with data mining objects either locally or remotely.  

docs/2014/analysis-services/data-mining/data-mining-concepts.md

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 # Data Mining Concepts
   Data mining is the process of discovering actionable information from large sets of data. Data mining uses mathematical analysis to derive patterns and trends that exist in data. Typically, these patterns cannot be discovered by traditional data exploration because the relationships are too complex or because there is too much data.

docs/2014/analysis-services/data-mining/data-mining-designer.md

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 # Data Mining Designer
   Data Mining Designer is the primary environment in which you work with mining models in [!INCLUDE[msCoName](../../includes/msconame-md.md)] [!INCLUDE[ssNoVersion](../../includes/ssnoversion-md.md)] [!INCLUDE[ssASnoversion](../../includes/ssasnoversion-md.md)]. You can access the designer either by selecting an existing mining structure, or by using the Data Mining Wizard to create a new mining structure and mining model. You can use Data Mining Designer to perform the following tasks:  

docs/2014/analysis-services/data-mining/data-mining-model-viewers.md

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 # Data Mining Model Viewers
   After you train a data mining model in [!INCLUDE[msCoName](../../includes/msconame-md.md)] [!INCLUDE[ssNoVersion](../../includes/ssnoversion-md.md)] [!INCLUDE[ssASnoversion](../../includes/ssasnoversion-md.md)], you can explore the model to look for interesting trends. Because the results of mining models are complex and can be difficult to understand in a raw format, visually investigating the data is often the easiest way to understand the rules and relationships that algorithms discover within the data.