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Like SQL, it supports a data definition language, data manipulation language and a data query language, all three with SQL-like syntax.
Whereas SQL statements operate on relational tables, DMX statements operate on data mining models.
Similarly, SQL Server supports the [[Multidimensional Expressions|MDX]] language for [[OLAP]] databases.
DMX is used to create and train data mining models, and to browse, manage, and predict against them.
DMX is composed of data definition language (DDL) statements, data manipulation language (DML) statements, and functions and operators.
==
DMX Queries are formulated using the <code>SELECT</code> statement.
They can extract information from existing data mining models in various ways.
== Data
The [[
* Create new data mining models and mining structures - <code>CREATE MINING STRUCTURE, CREATE MINING MODEL</code>
* Delete existing data mining models and mining structures - <code>DROP MINING STRUCTURE, DROP MINING MODEL</code>
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* Copy data from one mining model to another - <code>SELECT INTO</code>
== Data
The
* Train mining models - <code>INSERT INTO</code>
* Browse data in mining models - <code>SELECT FROM</code>
* Make predictions using mining model - <code>SELECT ... FROM PREDICTION JOIN</code>
== Example: a prediction query ==
This example is a singleton prediction query, which predicts for the given customer whether she will be interested in home loan products.
<source lang="tsql">
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[Decision Tree]
NATURAL PREDICTION JOIN
(SELECT
35 AS [Age],
'Y' AS [House Owner],
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</source>
== See also ==
*[[XML for Analysis]]
== External links ==
*[http://msdn2.microsoft.com/en-us/library/ms132058.aspx Data Mining Extensions (DMX) Reference], (at [[MSDN]])
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