{"id":8224,"date":"2018-02-15T13:48:09","date_gmt":"2018-02-15T13:48:09","guid":{"rendered":"https:\/\/data-flair.training\/blogs\/?p=8224"},"modified":"2021-05-28T13:02:07","modified_gmt":"2021-05-28T07:32:07","slug":"data-mining-query-language","status":"publish","type":"post","link":"https:\/\/data-flair.training\/blogs\/data-mining-query-language\/","title":{"rendered":"Data Mining Query Language (DMQL) – For Knowledge Discovery"},"content":{"rendered":"

In this Data Mining Tutorial<\/strong>, we will study Data Mining Query Language. As we will study every concept related to Query Language in Data Mining (DMQL), First we will study Query Language, Data Mining Query Language motivation and design.<\/p>\n

Further, will learn the syntax for every task and specification. Moreover, we will cover standardization and Query Language purposes.<\/p>\n

Introduction to Data Mining Query Language<\/h2>\n

It was proposed<\/span> by Han, Fu, Wang, et al. for the DBMiner data mining system. Although, it was based<\/span> on the structured Data Mining Query Language. These query languages are designed<\/span> to support ad hoc and interactive data mining. Also, it provides commands for specifying primitives.<\/p>\n

We can use Data Mining Query Language to work with databases and data warehouses as well. We can also use it to define data mining tasks. Particularly we examine how to define data warehouses and data marts in DMQL.<\/p>\n

Motivation to Data Mining Query Language<\/h2>\n

It can provide the ability to support ad-hoc and interactive data mining.<\/p>\n

Syntax of Data Mining Query Language<\/h2>\n

Syntax of DMQL for specifying task-relevant data<\/p>\n

use database database_name<\/pre>\n
or<\/p>\n
use data warehouse data_warehouse_name\r\nin relevance to att_or_dim_list\r\nfrom relation(s)\/cube(s) [where condition]\r\norder by order_list\r\ngroup by grouping_list<\/pre>\n
Syntax – Specifying Kind of Knowledge<\/h2>\n
Syntax for Characterization, Discrimination, Association, Classification, and Prediction.<\/p>\n
a. Data Mining Characterization<\/h3>\n
The syntax for characterization is \u2212<\/p>\n
mine characteristics [as pattern_name]\r\nanalyze {measure(s) }<\/pre>\n
The analyze clause, specifies aggregate<\/span> measures, such as count, sum, or count%<\/p>\n
b.\u00a0Data Mining\u00a0Discrimination<\/h3>\n
The syntax for Discrimination is \u2212<\/p>\n
mine comparison [as {pattern_name]}\r\nFor {target_class } where {t arget_condition }\r\n{versus {contrast_class_i }\r\nwhere {contrast_condition_i}}\r\nanalyze {measure(s) }<\/pre>\n
c. Data Mining Association<\/h3>\n
The syntax for Association is\u2212<\/p>\n
mine associations [ as {pattern_name} ]\r\n{matching {metapattern} }<\/pre>\n
d.\u00a0Data Mining Classification<\/h3>\n
The syntax for Classification is \u2212<\/p>\n
mine classification [as pattern_name]\r\nanalyze classifying_attribute_or_dimension<\/pre>\n
e.\u00a0Data Mining\u00a0Prediction<\/h3>\n
The syntax for prediction is \u2212<\/p>\n
mine prediction [as pattern_name]\r\nanalyze prediction_attribute_or_dimension\r\n{set {attribute_or_dimension_i= value_i}}<\/pre>\n
Syntax – Concept Hierarchy Specification<\/h2>\n
We use the following syntax to specify concept hierarchies\u2212<\/p>\n
use hierarchy <hierarchy> for <attribute_or_dimension><\/pre>\n
We use different syntaxes to define different types of hierarchies such as\u2212<\/p>\n
-schema hierarchies<\/strong><\/p>\n
define hierarchy time_hierarchy on date as [date,month quarter,year]<\/p>\n
– set-grouping hierarchies<\/strong><\/p>\n
define hierarchy age_hierarchy for age on customer as
\nlevel1: {young, middle_aged, senior} < level0: all
\nlevel2: {20, …, 39} < level1: young
\nlevel3: {40, …, 59} < level1: middle_aged
\nlevel4: {60, …, 89} < level1: senior<\/p>\n
-operation-derived hierarchies<\/strong><\/p>\n
define hierarchy age_hierarchy for age on customer
\nas {age_category(1), …, age_category(5)}
\n:= cluster(default, age, 5)< all(age)<\/p>\n
-rule-based hierarchies<\/strong><\/p>\n
define hierarchy profit_margin_hierarchy on item as
\nlevel_1: low_profit_margin < level_0: all<\/p>\n
if (price – cost)< $50
\nlevel_1: medium-profit_margin < level_0: all<\/p>\n
if ((price – cost) > $50) and ((price – cost) \u2264 $250))
\nlevel_1: high_profit_margin < level_0: all<\/p>\n
Syntax – Interestingness Measures Specification<\/h2>\n
Interestingness measures and thresholds can be specified by<\/span> the user with the statement \u2212<\/p>\n
with <interest_measure_name> threshold = threshold_value<\/pre>\n
Syntax – Pattern Presentation & Visualization Specification<\/h2>\n
We have a syntax, which allows users to specify the display of discovered patterns in one or more forms.<\/p>\n
display as <result_form><\/pre>\n
Data Mining Languages Standardization<\/h2>\n
This will serve the following purposes \u2212<\/p>\n
    \n
  • Basically, it helps the systematic development of data mining solutions.<\/li>\n<\/ul>\n
      \n
    • Also, improves interoperability among multiple<\/span> data mining systems and functions.<\/li>\n<\/ul>\n
        \n
      • Generally, it helps in Promoting education and rapid learning.<\/li>\n<\/ul>\n
          \n
        • Also, promotes the use of data mining systems in industry and society.<\/li>\n<\/ul>\n
          Purposes – Data Mining Query<\/h2>\n
          Data Mining Queries are useful for many purposes are:<\/p>\n
            \n
          • Apply the model to new data, to make single or multiple<\/span> predictions. You can provide input values as parameters, or in a batch.<\/li>\n<\/ul>\n
              \n
            • Get a statistical summary of the data used for training.<\/li>\n<\/ul>\n
                \n
              • Extract patterns and rule of the typical case representing a pattern in the model.<\/li>\n<\/ul>\n
                  \n
                • Extract regression formulas and other calculations that explain patterns.<\/li>\n<\/ul>\n
                    \n
                  • Get the cases that fit a particular pattern.<\/li>\n<\/ul>\n
                      \n
                    • Retrieve details about individual cases used in the model. Also, it includes data not used in an analysis.<\/li>\n<\/ul>\n
                        \n
                      • Retrain a model by adding new data, or perform cross-prediction.<\/li>\n<\/ul>\n
                        Conclusion<\/h2>\n
                        As a result, we have studied Data mining Query Language for Knowledge Discovery<\/strong> and it’s all related concepts. Also, we have covered all syntax’s specification and standardization along with Query Language purposes.<\/p>\n
                        I hope this blog will help you to understand the concept of Data Mining Query Language (DMQL). Furthermore, if you feel any query feel free to ask in a comment section.<\/p>\n","protected":false},"excerpt":{"rendered":"
                        In this Data Mining Tutorial, we will study Data Mining Query Language. As we will study every concept related to Query Language in Data Mining (DMQL), First we will study Query Language, Data Mining...<\/p>\n","protected":false},"author":6,"featured_media":8225,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[18],"tags":[150,3366,3367,4014,10258,11097,13185,13186],"class_list":["post-8224","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-mining","tag-a-data-mining-query-language","tag-data-mining-queries","tag-data-mining-query-language","tag-dmql","tag-purposes-data-mining-queries","tag-query-language-in-data-mininig","tag-specifying-kind-of-knowledge","tag-specifying-kind-of-knowledge-syntax"],"yoast_head":"\nData Mining Query Language (DMQL) - For Knowledge Discovery - DataFlair<\/title>\n<meta name=\"description\" content=\"What is Data Mining Query Language- syntax of DMQL, purpose of data mining query, Data Mining Languages Standardization, Pattern Presentation\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/data-flair.training\/blogs\/data-mining-query-language\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Data Mining Query Language (DMQL) - 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