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:

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. 2. 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