Min-max normalization, (usually called feature scaling) performs a linear transformation on the original data. This technique gets all the scaled data in the range [0,1]. The formula to achieve this is the following:
Min-max normalization preserves the relationships among the original data values. The cost of having this bounded range is that we will end up with smaller standard deviations, which can suppress the effect of outliers.
To normalize the data, we use the scale() function, which is a generic function whose default method centers and/or scales the columns of a numeric matrix. The function syntax is as follows: ...
