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docs/dmx/select-distinct-from-model-dmx.md

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## Syntax
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```
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SELECT [FLATTENED] DISTINCT [TOP <n>] <expression list> FROM <model>
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[WHERE <condition list>][ORDER BY <expression>]
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```
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|Gender|
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|------------|
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|F|
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|M|
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|Midpoint Age|Minimum Age|Maximum Age|
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|------------------|-----------------|-----------------|
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|62|26|97|
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The query also returns a single row of null values, to represent missing values.
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## Discretized Column Example
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The following code sample returns the midpoint, maximum, and minimum values for each bucket that has been created by the algorithm for the column, [`Yearly Income]`. To reproduce the results for this example, you must create a new mining structure that is the same as `[Targeted Mailing]`. In the wizard, change the content type of the `Yearly Income` column from **Continuous** to **Discretized**.
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The following code sample returns the midpoint, maximum, and minimum values for each bucket that has been created by the algorithm for the column, `[Yearly Income]`. To reproduce the results for this example, you must create a new mining structure that is the same as `[Targeted Mailing]`. In the wizard, change the content type of the `Yearly Income` column from **Continuous** to **Discretized**.
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> [!NOTE]
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> You can also change the mining model created in the Basic Mining Tutorial to discretize the mining structure column, [`Yearly Income]`. For information about how to do this, see [Change the Discretization of a Column in a Mining Model](/analysis-services/data-mining/change-the-discretization-of-a-column-in-a-mining-model). However, when you change the discretization of the column, it will force the mining structure to be reprocessed, which will change the results of other models that you have built using that structure.
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> You can also change the mining model created in the Basic Mining Tutorial to discretize the mining structure column, `[Yearly Income]`. For information about how to do this, see [Change the Discretization of a Column in a Mining Model](/analysis-services/data-mining/change-the-discretization-of-a-column-in-a-mining-model). However, when you change the discretization of the column, it will force the mining structure to be reprocessed, which will change the results of other models that you have built using that structure.
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```
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SELECT DISTINCT [Yearly Income] AS [Bucket Average],
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|Bucket Average|Bucket Minimum|Bucket Maximum|
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|--------------------|--------------------|--------------------|
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|24610.7|10000|39221.41|
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|55115.73|39221.41|71010.05|
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|84821.54|71010.05|98633.04|
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|111633.9|98633.04|124634.7|
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|147317.4|124634.7|170000|
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You can see that the values of the [Yearly Income] column have been discretized into five buckets, plus an additional row of null values, to represent missing values.
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You can see that the values of the `[Yearly Income]` column have been discretized into five buckets, plus an additional row of null values, to represent missing values.
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The number of decimal places in the results depends on the client that you use for querying. Here they have been rounded to two decimal places, both for simplicity and to reflect the values that are displayed in [!INCLUDE[ssBIDevStudioFull](../includes/ssbidevstudiofull-md.md)].
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