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;
After applying the AVC set table looks like;
AVC sets can be built according to the amount of main memory available. This can be described in the following three cases;
- 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.
Next Similar Tutorials
- Decision tree induction on categorical attributes – Click Here
- Decision Tree Induction and Entropy in data mining – Click Here
- Overfitting of decision tree and tree pruning – Click Here
- Attribute selection Measures – Click Here
- Computing Information-Gain for Continuous-Valued Attributes in data mining – Click Here
- Gini index for binary variables – Click Here
- Bagging and Bootstrap in Data Mining, Machine Learning – Click Here
- Evaluation of a classifier by confusion matrix in data mining – Click Here
- Holdout method for evaluating a classifier in data mining – Click Here
- RainForest Algorithm / Framework – Click Here
- Boosting in data mining – Click Here
- Naive Bayes Classifier – Click Here