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


Income Rank Buy_Mobile
75,000 Professor yes
75,000 Professor yes
50,000 Lecturer no

After applying the AVC set table looks like;

[quads id=1]


Income Buy_Mobile
Yes No
75,000 2 0
50,000 0 1
Rank Buy_Mobile
Yes No
Professor 2 0
Lecturer 0 1


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

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    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
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    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


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