A Novel Decision Tree Approach for the Prediction of Precipitation Using Entropy in SLIQ
Narasimha Prasad L V and Prudhvi Kumar Reddy K (Vardhaman College of Engineering, India); Naidu M m (S V University College of Engineering, Malaysia)
The rising tendency of population of every nation is one of the severe stumbling blocks to arrest its economic growth, not able to address even the basic needs of its people. It is high time to have introspection for the deficiencies and find a remedy. The major basic need is food, a product of agriculture. Agriculture mainly depends on rainfall. Data mining is an emerging, efficient, easily implementable tool, which predicts the useful patterns for the prediction of rainfall in a very short time. Supervised Learning In Quest, an efficient data mining decision tree is applied in the prediction of precipitation. The present research illustrates the Supervised Learning In Quest decision tree algorithm using entropy, which estimates the prediction of precipitation with an average accuracy of 74.92% and the knowledge extraction is purely based on historical data.
Journal: International Journal of Simulation- Systems, Science and Technology- IJSSST V13
Published: Feb 27, 2012