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‎docs/dmx/alter-mining-structure-dmx.md‎

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The name of a data mining algorithm, as defined by the provider.
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> [!NOTE]
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> A list of the algorithms supported by the current provider can be retrieved by using [DMSCHEMA_MINING_SERVICES Rowset](https://docs.microsoft.com/previous-versions/sql/sql-server-2012/ms126251(v=sql.110)). To view the algorithms supported in the current instance of [!INCLUDE[ssASnoversion](../includes/ssasnoversion-md.md)], see [Data Mining Properties](https://docs.microsoft.com/analysis-services/server-properties/data-mining-properties).
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> A list of the algorithms supported by the current provider can be retrieved by using [DMSCHEMA_MINING_SERVICES Rowset](/previous-versions/sql/sql-server-2012/ms126251(v=sql.110)). To view the algorithms supported in the current instance of [!INCLUDE[ssASnoversion](../includes/ssasnoversion-md.md)], see [Data Mining Properties](/analysis-services/server-properties/data-mining-properties).
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*parameter list*
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Optional. A comma-separated list of provider-defined parameters for the algorithm.
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```
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### Column Name and Alias
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The column name that you use in the column definition list must be the name of the column as it is used in the mining structure. However, you can optionally define an alias to represent the structure column in the mining model. You can also create multiple column definitions for the same structure column, and assign a different alias and prediction usage to each copy of the column. By default, the structure column name is used if you do not define an alias. For more information, see [Create an Alias for a Model Column](https://docs.microsoft.com/analysis-services/data-mining/create-an-alias-for-a-model-column).
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The column name that you use in the column definition list must be the name of the column as it is used in the mining structure. However, you can optionally define an alias to represent the structure column in the mining model. You can also create multiple column definitions for the same structure column, and assign a different alias and prediction usage to each copy of the column. By default, the structure column name is used if you do not define an alias. For more information, see [Create an Alias for a Model Column](/analysis-services/data-mining/create-an-alias-for-a-model-column).
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For nested table columns, you specify the name of the nested table, specify the data type as **TABLE**, and then provide the list of nested columns to include in the model, enclosed in parentheses.
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Filter criteria expressions are simplified DMX predicates, similar to a WHERE clause. Filter expressions are restricted to formulas that use basic mathematical operators, scalars, and column names. The exception is the EXISTS operator; it evaluates to true if at least one row is returned for the subquery. Predicates can be combined by using the common logical operators: AND, OR, and NOT.
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For more information about filters used with mining models, see [Filters for Mining Models &#40;Analysis Services - Data Mining&#41;](https://docs.microsoft.com/analysis-services/data-mining/filters-for-mining-models-analysis-services-data-mining).
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For more information about filters used with mining models, see [Filters for Mining Models &#40;Analysis Services - Data Mining&#41;](/analysis-services/data-mining/filters-for-mining-models-analysis-services-data-mining).
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> [!NOTE]
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> Columns in a filter must be mining structure columns. You cannot create a filter on a model column or an aliased column.
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For more information about DMX operators and syntax, see [Mining Model Columns](https://docs.microsoft.com/analysis-services/data-mining/mining-model-columns).
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For more information about DMX operators and syntax, see [Mining Model Columns](/analysis-services/data-mining/mining-model-columns).
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## Parameter Definition List
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You can adjust the performance and functionality of a model by adding algorithm parameters to the parameter list. The parameters that you can use depend on the algorithm that you specify in the USING clause. For a list of parameters that are associated with each algorithm, see [Data Mining Algorithms &#40;Analysis Services - Data Mining&#41;](https://docs.microsoft.com/analysis-services/data-mining/data-mining-algorithms-analysis-services-data-mining).
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You can adjust the performance and functionality of a model by adding algorithm parameters to the parameter list. The parameters that you can use depend on the algorithm that you specify in the USING clause. For a list of parameters that are associated with each algorithm, see [Data Mining Algorithms &#40;Analysis Services - Data Mining&#41;](/analysis-services/data-mining/data-mining-algorithms-analysis-services-data-mining).
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The syntax of the parameter list is as follows:
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[Data Mining Extensions &#40;DMX&#41; Data Manipulation Statements](../dmx/dmx-statements-data-manipulation.md)
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[Data Mining Extensions &#40;DMX&#41; Statement Reference](../dmx/data-mining-extensions-dmx-statements.md)
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‎docs/dmx/bottomcount-dmx.md‎

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The value that is supplied by the \<rank expression> argument determines the increasing order of rank for the rows that are supplied in the \<table expression> argument, and the number of bottom-most rows that is specified in the \<count> argument is returned.
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## Examples
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The following example creates a prediction query against the Association model that you build by using the [Basic Data Mining Tutorial](https://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c).
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The following example creates a prediction query against the Association model that you build by using the [Basic Data Mining Tutorial](/previous-versions/sql/sql-server-2016/ms167167(v=sql.130)).
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To understand how BottomCount works, it might be helpful to first execute a prediction query that returns only the nested table.
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```
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> [!NOTE]
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> In this example, the value supplied as input contains a single quotation mark, and therefore must be escaped by prefacing it with another single quotation mark. If you are not sure of the syntax for inserting an escape character, you can use the Prediction Query Builder to create the query. When you select the value from the dropdown list, the required escape character is inserted for you. For more information, see [Create a Singleton Query in the Data Mining Designer](https://docs.microsoft.com/analysis-services/data-mining/create-a-singleton-query-in-the-data-mining-designer).
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> In this example, the value supplied as input contains a single quotation mark, and therefore must be escaped by prefacing it with another single quotation mark. If you are not sure of the syntax for inserting an escape character, you can use the Prediction Query Builder to create the query. When you select the value from the dropdown list, the required escape character is inserted for you. For more information, see [Create a Singleton Query in the Data Mining Designer](/analysis-services/data-mining/create-a-singleton-query-in-the-data-mining-designer).
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Example results:
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[BottomSum &#40;DMX&#41;](../dmx/bottomsum-dmx.md)
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[TopCount &#40;DMX&#41;](../dmx/topcount-dmx.md)
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‎docs/dmx/bottompercent-dmx.md‎

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The **BottomPercent** function returns the bottom-most rows in increasing order of rank. The rank is based on the evaluated value of the \<rank expression> argument for each row, such that the sum of the \<rank expression> values is at least the given percentage that is specified by the \<percent> argument. **BottomPercent** returns the smallest number of elements possible while still meeting the specified percent value.
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## Examples
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The following example creates a prediction query against the Association model that you built in the [Basic Data Mining Tutorial](https://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c).
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The following example creates a prediction query against the Association model that you built in the [Basic Data Mining Tutorial](/previous-versions/sql/sql-server-2016/ms167167(v=sql.130)).
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To understand how BottomPercent works, it may be helpful to first execute a prediction query that returns only the nested table.
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```
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> [!NOTE]
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> In this example, the value supplied as input contains a single quotation mark, and therefore must be escaped by prefacing it with another single quotation mark. If you are not sure of the syntax for inserting an escape character, you can use the Prediction Query Builder to create the query. When you select the value from the dropdown list, the required escape character is inserted for you. For more information, see [Create a Singleton Query in the Data Mining Designer](https://docs.microsoft.com/analysis-services/data-mining/create-a-singleton-query-in-the-data-mining-designer).
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> In this example, the value supplied as input contains a single quotation mark, and therefore must be escaped by prefacing it with another single quotation mark. If you are not sure of the syntax for inserting an escape character, you can use the Prediction Query Builder to create the query. When you select the value from the dropdown list, the required escape character is inserted for you. For more information, see [Create a Singleton Query in the Data Mining Designer](/analysis-services/data-mining/create-a-singleton-query-in-the-data-mining-designer).
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[Data Mining Extensions &#40;DMX&#41; Function Reference](../dmx/data-mining-extensions-dmx-function-reference.md)
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[Functions &#40;DMX&#41;](../dmx/functions-dmx.md)
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‎docs/dmx/bottomsum-dmx.md‎

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The **BottomSum** function returns the bottom-most rows in increasing order of rank. The rank is based on the evaluated value of the \<rank expression> argument for each row, such that the sum of the \<rank expression> values is at least the given total that is specified by the \<sum> argument. **BottomSum** returns the smallest number of elements possible while still meeting the specified sum value.
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## Examples
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The following example creates a prediction query against the Association model that you build by using the [Basic Data Mining Tutorial](https://msdn.microsoft.com/library/6602edb6-d160-43fb-83c8-9df5dddfeb9c).
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The following example creates a prediction query against the Association model that you build by using the [Basic Data Mining Tutorial](/previous-versions/sql/sql-server-2016/ms167167(v=sql.130)).
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```
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> [!NOTE]
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> In this example, the value supplied as input contains a single quotation mark, and therefore must be escaped by prefacing it with another single quotation mark. If you are not sure of the syntax for inserting an escape character, you can use the Prediction Query Builder to create the query. When you select the value from the dropdown list, the required escape character is inserted for you. For more information, see [Create a Singleton Query in the Data Mining Designer](https://docs.microsoft.com/analysis-services/data-mining/create-a-singleton-query-in-the-data-mining-designer).
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> In this example, the value supplied as input contains a single quotation mark, and therefore must be escaped by prefacing it with another single quotation mark. If you are not sure of the syntax for inserting an escape character, you can use the Prediction Query Builder to create the query. When you select the value from the dropdown list, the required escape character is inserted for you. For more information, see [Create a Singleton Query in the Data Mining Designer](/analysis-services/data-mining/create-a-singleton-query-in-the-data-mining-designer).
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[General Prediction Functions &#40;DMX&#41;](../dmx/general-prediction-functions-dmx.md)
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[BottomPercent &#40;DMX&#41;](../dmx/bottompercent-dmx.md)
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‎docs/dmx/clusterdistance-dmx.md‎

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## Remarks
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The **ClusterDistance** function returns the distance between the input case and the cluster that has the highest probability for that input case.
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In case of K-Means clustering, since any case can belong to only one cluster, with a membership weight of 1.0, the cluster distance is always 0. However, in K-Means, each cluster is assumed to have a centroid. You can obtain the value of the centroid by querying or browsing the NODE_DISTRIBUTION nested table in the mining model content. For more information, see [Mining Model Content for Clustering Models &#40;Analysis Services - Data Mining&#41;](https://docs.microsoft.com/analysis-services/data-mining/mining-model-content-for-clustering-models-analysis-services-data-mining).
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In case of K-Means clustering, since any case can belong to only one cluster, with a membership weight of 1.0, the cluster distance is always 0. However, in K-Means, each cluster is assumed to have a centroid. You can obtain the value of the centroid by querying or browsing the NODE_DISTRIBUTION nested table in the mining model content. For more information, see [Mining Model Content for Clustering Models &#40;Analysis Services - Data Mining&#41;](/analysis-services/data-mining/mining-model-content-for-clustering-models-analysis-services-data-mining).
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In the case of the default EM clustering method, all the points inside the cluster are considered equally likely; therefore, by design there is no centroid for the cluster. The value of **ClusterDistance** between a particular case and a particular cluster *N* is calculated as follows:
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[Cluster &#40;DMX&#41;](../dmx/cluster-dmx.md)
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[Data Mining Extensions &#40;DMX&#41; Function Reference](../dmx/data-mining-extensions-dmx-function-reference.md)
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[Functions &#40;DMX&#41;](../dmx/functions-dmx.md)
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[Mining Model Content for Clustering Models &#40;Analysis Services - Data Mining&#41;](https://docs.microsoft.com/analysis-services/data-mining/mining-model-content-for-clustering-models-analysis-services-data-mining)
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[Mining Model Content for Clustering Models &#40;Analysis Services - Data Mining&#41;](/analysis-services/data-mining/mining-model-content-for-clustering-models-analysis-services-data-mining)
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‎docs/dmx/clusterprobability-dmx.md‎

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SELECT NODE_CAPTION FROM <model>.CONTENT
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For more information about using this syntax, see [SELECT FROM &#60;model&#62;.CONTENT &#40;DMX&#41;](../dmx/select-from-model-content-dmx.md). For more information about the mining model content schema rowset, see [DMSCHEMA_MINING_MODEL_CONTENT Rowset](https://docs.microsoft.com/previous-versions/sql/sql-server-2012/ms126267(v=sql.110)).
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For more information about using this syntax, see [SELECT FROM &#60;model&#62;.CONTENT &#40;DMX&#41;](../dmx/select-from-model-content-dmx.md). For more information about the mining model content schema rowset, see [DMSCHEMA_MINING_MODEL_CONTENT Rowset](/previous-versions/sql/sql-server-2012/ms126267(v=sql.110)).
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If a \<node caption> is not specified, the function returns the probability that the input cases belong to the most likely cluster. Use the **Cluster** function to return the most likely cluster.
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[Functions &#40;DMX&#41;](../dmx/functions-dmx.md)
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[General Prediction Functions &#40;DMX&#41;](../dmx/general-prediction-functions-dmx.md)
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‎docs/dmx/content-types-dmx.md‎

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Each algorithm supports specific content types. For example, the [!INCLUDE[msCoName](../includes/msconame-md.md)] Naive Bayes algorithm cannot use continuous columns. To use a continuous column in a [!INCLUDE[msCoName](../includes/msconame-md.md)] Naive Bayes model, you must discretize the data in the column. Some algorithms require certain content types in order to function correctly. For example, the [!INCLUDE[msCoName](../includes/msconame-md.md)] Time Series algorithm requires a key time column to identify the time over which the data was collected.
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For a complete description of the content types that [!INCLUDE[ssASnoversion](../includes/ssasnoversion-md.md)] supports, see [Content Types &#40;Data Mining&#41;](https://docs.microsoft.com/analysis-services/data-mining/content-types-data-mining).
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## See Also
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[Data Mining Algorithms &#40;Analysis Services - Data Mining&#41;](https://docs.microsoft.com/analysis-services/data-mining/data-mining-algorithms-analysis-services-data-mining)
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[Data Mining Algorithms &#40;Analysis Services - Data Mining&#41;](/analysis-services/data-mining/data-mining-algorithms-analysis-services-data-mining)
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[Data Mining Extensions &#40;DMX&#41; Reference](../dmx/data-mining-extensions-dmx-reference.md)
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[Data Mining Extensions &#40;DMX&#41; Syntax Elements](../dmx/data-mining-extensions-dmx-syntax-elements.md)
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[Data Mining Extensions &#40;DMX&#41; Function Reference](../dmx/data-mining-extensions-dmx-function-reference.md)
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[Structure and Usage of DMX Prediction Queries](../dmx/structure-and-usage-of-dmx-prediction-queries.md)
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