14- Constraint Based Association Mining constraints - conditions Types of Constraints 1- Knowledge Type - specifies the type of knowledge on which you want to do the mining association, correlation, regression, etcetera 2- Data constraints - specifies the type of data on which you want to generate the rules -only task relevant data 3- Dimension / level constraints - specifies the dimension or the level on which to generate the association rules -the concept hierarchy -an attribute as a dimension -at which level of abstraction 4- Interestingness constraints - -support, confidence are used to identify 5- Rule constraints - specifies the form of rules to be mined to generate the association rules -Metarules guided mining (rules about rules) users may specify the syntactic form of rules in the form of a constraint -Constraint pushing - specify the relationships of the variables which are in the rules between variables, as well as which variables should not be used together -Metarules and Constraint pushing may be used together .
Excuse me but i think due to pressure and request by students you are making videos very quickly, but you are saying whatever coming to your mouth please dont do that wrong information will mislead. Actually all videos are good but saying few wrong points.These are very helpful for students thanks.
hi, i didnt feel any of the topics were particularly wrong but yes i can see she is in a hurry to get the videos out for the students for exams , plz share which part your talking about like where are u finding the mistake
Tysm Mam 😁
Love to learn from you in such a systematic manner 🥳❤
Hello can u make the videos fast we are having exam on 11 feb plzzz your videos are soo helpfull.🙏
Love to learn from you in such a systematic manner
Mam where we can take notes which you are used in vedios
14- Constraint Based Association Mining
constraints - conditions
Types of Constraints
1- Knowledge Type - specifies the type of knowledge on which you want to do the mining
association, correlation, regression, etcetera
2- Data constraints - specifies the type of data on which you want to generate the rules
-only task relevant data
3- Dimension / level constraints - specifies the dimension or the level on which to
generate the association rules
-the concept hierarchy
-an attribute as a dimension
-at which level of abstraction
4- Interestingness constraints -
-support, confidence are used to identify
5- Rule constraints - specifies the form of rules to be mined
to generate the association rules
-Metarules guided mining (rules about rules)
users may specify the syntactic form of rules in the form of a constraint
-Constraint pushing - specify the relationships of the variables which are
in the rules between variables, as well as which variables should not be used together
-Metarules and Constraint pushing may be used together
.
make videos on FUNDAMENTALS OF BIOMEDICAL APPLICATIONS
Can I get notes on
LvU ❤️
You missed the syntactic rule constraints
Excuse me but i think due to pressure and request by students you are making videos very quickly, but you are saying whatever coming to your mouth please dont do that wrong information will mislead. Actually all videos are good but saying few wrong points.These are very helpful for students thanks.
hi, i didnt feel any of the topics were particularly wrong but yes i can see she is in a hurry to get the videos out for the students for exams ,
plz share which part your talking about like where are u finding the mistake
Hello can you say the part what is wrong without reason don't overexicte
Hindi me bhi bta diya kro ji