Explain the data mining language.

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  • The Data Mining Query Language is actually based on the Structured Query Language (SQL). Data Mining Query Languages can be designed to support ad hoc and interactive data mining. This DMQL provides commands for specifying primitives. The DMQL can work with databases and data warehouses as well.
  • DMQL can be used to define data mining tasks. Particularly we examine how to define data warehouses and data marts in DMQL.


Here is the syntax of DMQL for specifying task-relevant data −

use database database_name


or,


use data warehouse data_warehouse_name

in relevance to att_or_dim_list

from relation(s)/cube(s) [where condition]

order by order_list

group by grouping_list


Characterization

The syntax for characterization is −

mine characteristics [as pattern_name]

analyze {measure(s) }

The analyze clause, specifies aggregate measures, such as count, sum, or count%.


Discrimination

The syntax for Discrimination is −

mine comparison [as {pattern_name]}

For {target_class } where {t arget_condition }

{versus {contrast_class_i }

where {contrast_condition_i}}

analyze {measure(s) }


Association

The syntax for Association is−

mine associations [ as {pattern_name} ]

{matching {metapattern} }


Prediction

The syntax for prediction is −

mine prediction [as pattern_name]

analyze prediction_attribute_or_dimension

{set {attribute_or_dimension_i= value_i}}

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