Various form of data normalization

An attribute is normalized by scaling its values so that they fall within a small specified range, such as 0.0 to1.0 Normalization is used for classification algorithms involving neural networks or distance measurements such as nearest-neighbor classification and clustering. If using the neural network backpropagation algorithm for classification mining, normalizing the input values for each attribute measured in the training tuples will help speed up the learning phase. For distance-based methods, normalization helps prevent attributes with initially large ranges from outweighing attributes with initially smaller ranges. There are many methods for data normalization. Some of them are as follows

1. Min-max Normalization
2. Z-score Normalization
3. Decimal Scaling Normalization