RainForest Algorithm / Framework – (Data Mining)

RainForest Algorithm / Framework – (Data Mining)

RainForest is framework specially designed to classify the large data set.

RainForest contains AVC set.

AVC set consist of the following parts;

  1. Attribute
  2. Value
  3. Class_Label

Example:

IncomeRankBuy_Mobile
75,000Professoryes
75,000Professoryes
50,000Lecturerno

After applying the AVC set table looks like;

[quads id=1]

 

IncomeBuy_Mobile
YesNo
75,00020
50,00001
RankBuy_Mobile
YesNo
Professor20
Lecturer01

 

AVC sets can be built according to the amount of main memory available. This can be described in the following three cases;

  1. The AVC-set of the root node fits in main memory. rain forest classification2. Each individual AVC-set of the root node fits in main memory, but the AVC-group of the root node does not fit in main memory.
    3. None of the individual AVC-sets of the root fit in the main memory.

  2. Next Similar Tutorials

    1. Decision tree induction on categorical attributes  – Click Here
    2. Decision Tree Induction and Entropy in data mining – Click Here
    3. Overfitting of decision tree and tree pruning – Click Here
    4. Attribute selection Measures – Click Here
    5. Computing Information-Gain for Continuous-Valued Attributes in data mining – Click Here
    6. Gini index for binary variables – Click Here
    7. Bagging and Bootstrap in Data Mining, Machine Learning – Click Here
    8. Evaluation of a classifier by confusion matrix in data mining – Click Here
    9. Holdout method for evaluating a classifier in data mining – Click Here
    10. RainForest Algorithm / Framework – Click Here
    11. Boosting in data mining – Click Here
    12. Naive Bayes Classifier  – Click Here

     

Subscribe for Friendship

Latest posts by Prof. Fazal Rehman Shamil (see all)
Share to support

Buy advertisement space on T4Tutorials

For more details email [email protected]