Min–Max Normalization Method

Normalization is a scaling approach to shift and rescale the values of datasets. The min–max normalization method was applied to scale the data in the range between 0 and 1. The normalization method was applied for the overlap of the entire dataset using the following equation:

$$\dot{\mathcal{V}} = \frac{V - \mathbf{x\_{min}}}{\max(\mathbf{A}) - \min(\mathbf{A})} (\text{new\\_max}(\mathbf{A}) - \text{new\\_min}(\mathbf{A})) + \text{new\\_min}(\mathbf{A}) \tag{1}$$

where, min(A) and max(A) are the minimum and maximum data, respectively, new\_min(A) and new\_max(A) are the new values of the minimum and maximum used for the scaling of the data, and *V* ´ is the normalized data.
