An Approach to Automation Selection of Decision Tree Based on Training Data Set
In Data mining applications, very large training data sets with several million records are common. Decision trees are very much powerful and excellent technique for both classification and prediction problems. Many decision tree construction algorithms have been proposed to develop and handle large or small training data. Some related algorithms are best for large data sets and some for small data sets. Each algorithm works best for its own criteria. The decision tree algorithms classify categorical and continuous attributes very well but it handles efficiently only a smaller data set. It consumes more time for large datasets.