question: with the below example extract the Mining classification rules through the given DMQL$.$ Write a math solutions and anote $($not code$)$ in pen and paper $(2-3$ pages$).$

As a marketing manager of AllElectronics, you would like

to classify customers based on their buying patterns.

You are interested in customers whose salary $>=$

$$40,000,$ and who have bought $>$ $$1,000$ worth of items,

each of which is priced $>=$ $$100.$

You are interested in the customer$$s age, income, the

types of items purchased, the purchase location, and

where the items were made.

You would like to view the resulting classification in the

form of rules.

This data mining query is expressed in DMQL as follows.

Mining classification rules

use database AllElectronics$\_$db

use location$\_$hierarchy for T$.$branch, age$\_$hierarchy for C$.$age

mine classification as promising customers

in relevance to C$.$age, C$.$income, I.type, I.place$\_$made,

T$.$branch

from customer C$,$ item I, transaction T

where I.item$\_$ID $=$ T$.$item$\_$ID and C$.$cust$\_$ID $=$ T$.$cust$\_$ID

and C$.$income $>=40,000$ and I.price $>=100$

group by T$.$cust$\_$ID

having sum$($I$.$price$)>=1,000$

display as rules

Miningclassification rules

The data mining query is parsed to form an SQL query that

retrieves the set of task$-$relevant data specified in lines $1,4$ to $8.$

Line $1$ specifies the All$-$Electronics database, line $4$ lists the

relevant attributes $($i$.$e$.,$ on which mining is to be performed$)$ from

the relations specified in line $5$ for the conditions given in lines $6$

and $7.$

Line $2$ specifies that the concept hierarchies location hierarchy

and age hierarchy be used as background knowledge to

generalize branch locations and customer age values,

respectively.

Line $3$ specifies that the kind of knowledge to be mined for this

task is classification. Note that we want to generate a

classification model for $$promising customers$$ versus $$non

promising customers.$$

Miningclassification rules

In classification, often, an attribute may be specified as the

class label attribute, whose values explicitly represent the

classes.

However, in this example, the two classes are implicit.

The specified data are retrieved and considered examples

of $$promising customers,$$ the remaining customers in the

customer table are considered as $$non$-$promising.$$

Classification is performed based on this training set.

Line $9$ specifies that the mining results are to be displayed

as a set of rules.