Data Mining - Query Language

The Data Mining Query Language (DMQL) was proposed by Han, Fu, Wang, et al. for the DBMiner data mining system. 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

Here we will discuss the syntax for Characterization, Discrimination, Association, Classification, and Prediction.
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%.
For example −
Description describing customer purchasing habits.
mine characteristics as customerPurchasing
analyze count%

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) }

For example, a user may define big spenders as customers who purchase items that cost $100 or more on an average; and budget spenders as customers who purchase items at less than $100 on an average. The mining of discriminant descriptions for customers from each of these categories can be specified in the DMQL as −
mine comparison as purchaseGroups
for bigSpenders where avg(I.price) ≥$100
versus budgetSpenders where avg(I.price)< $100
analyze count

The syntax for Association is−
mine associations [ as {pattern_name} ]
{matching {metapattern} }

For Example −
mine associations as buyingHabits
matching P(X:customer,W) ^ Q(X,Y) ≥ buys(X,Z)

where X is key of customer relation; P and Q are predicate variables; and W, Y, and Z are object variables.
The syntax for Classification is −
mine classification [as pattern_name]
analyze classifying_attribute_or_dimension

For example, to mine patterns, classifying customer credit rating where the classes are determined by the attribute credit_rating, and mine classification is determined as classifyCustomerCreditRating.
analyze credit_rating

The syntax for prediction is −
mine prediction [as pattern_name]
analyze prediction_attribute_or_dimension
{set {attribute_or_dimension_i= value_i}}

To specify concept hierarchies, use the following syntax −
use hierarchy <hierarchy> for <attribute_or_dimension>

We use different syntaxes to define different types of hierarchies such as−
-schema hierarchies
define hierarchy time_hierarchy on date as [date,month quarter,year]
-
set-grouping hierarchies
define hierarchy age_hierarchy for age on customer as
level1: {young, middle_aged, senior} < level0: all
level2: {20, ..., 39} < level1: young
level3: {40, ..., 59} < level1: middle_aged
level4: {60, ..., 89} < level1: senior

-operation-derived hierarchies
define hierarchy age_hierarchy  for age  on customer  as
{age_category(1), ..., age_category(5)} 
:= cluster(default, age, 5) < all(age)

-rule-based hierarchies
define hierarchy profit_margin_hierarchy  on item  as
level_1: low_profit_margin < level_0:  all

if (price - cost)< $50
   level_1:  medium-profit_margin < level_0:  all
   
if ((price - cost) > $50)  and ((price - cost) ≤ $250)) 
   level_1:  high_profit_margin < level_0:  all

Interestingness measures and thresholds can be specified by the user with the statement −
with <interest_measure_name>  threshold = threshold_value

For Example −
with support threshold = 0.05
with confidence threshold = 0.7

We have a syntax, which allows users to specify the display of discovered patterns in one or more forms.
display as <result_form>

For Example −
display as table

As a market manager of a company, you would like to characterize the buying habits of customers who can purchase items priced at no less than $100; with respect to the customer's age, type of item purchased, and the place where the item was purchased. You would like to know the percentage of customers having that characteristic. In particular, you are only interested in purchases made in Canada, and paid with an American Express credit card. You would like to view the resulting descriptions in the form of a table.
use database AllElectronics_db
use hierarchy location_hierarchy for B.address
mine characteristics as customerPurchasing
analyze count%
in relevance to C.age,I.type,I.place_made
from customer C, item I, purchase P, items_sold S,  branch B
where I.item_ID = S.item_ID and P.cust_ID = C.cust_ID and
P.method_paid = "AmEx" and B.address = "Canada" and I.price ≥ 100
with noise threshold = 5%
display as table

Standardizing the Data Mining Languages will serve the following purposes −
Helps systematic development of data mining solutions.
Helps systematic development of data mining solutions.
Improves interoperability among multiple data mining systems and functions.
Improves interoperability among multiple data mining systems and functions.
Promotes education and rapid learning.
Promotes education and rapid learning.
Promotes the use of data mining systems in industry and society.
Promotes the use of data mining systems in industry and society.

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