Min Max Normalization in data mining

Min Max is a data normalization technique like Z score, decimal scaling, and normalization with standard deviation. It helps to normalize the data. It will scale the data between 0 and 1. This normalization helps us to understand the data easily.

For example, if I say you to tell me the difference between 200 and 1000 then it’s a little bit confusing as compared to when I ask you to tell me the difference between 0.2 and 1.

Min Max normalization formula

marks

8
10
15
20  

Min:

The minimum value of the given attribute. Here Min is 8

Max:

The maximum value of the given attribute. Here Max is 20

V: V is the respective value of the attribute. For example here V1=8, V2=10, V3=15, and V4=20

newMax:

1

newMin:

0

normalization in data mining

 

min max normalization scalling
Figure: min-max normalization scaling

marks

marks after Min-Max normalization

8 0
10 0.16
15 0.58
20 1

Min max normalization example

Download Excel File Calculations

 

Min Max Normalization Excel File Calculations

Comparison of Min-Max Normalization and Z-Score Normalization

Let’s see the comparison of Min-Max Normalization and Z-Score Normalization

Min-max normalization Z-score normalization
Not very well efficient in handling the outliers Handles the outliers in a good way.
Min-max Guarantees that all the features will have the exact same scale. Helpful in the normalization of the data but not with the exact same scale.