# 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.

## 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

8 0
10 0.16
15 0.58
20 1

E

## Example #2

Normalize the following data;

 Rollno programming database stats data mining 133 55 33 4 55 134 44 56 34 33

After Normalization:

 Rollno Programming Database Stats Data Mining 133 0.6667 0 0 1 134 0 1 1 0
1. Find the minimum and maximum values for each attribute:
• Programming: min = 44, max = 55
• Database: min = 33, max = 56
• Stats: min = 4, max = 34
• Data Mining: min = 33, max = 55
2. Apply the min-max normalization formula for each value:
• For the first row, Rollno is not normalized, so we leave it as it is.
• For Programming in the first row: (55 – 44) / (55 – 44) = 0.6667
• For Database in the first row: (33 – 33) / (56 – 33) = 0.0000
• For Stats in the first row: (4 – 4) / (34 – 4) = 0.0000
• For Data Mining in the first row: (55 – 33) / (55 – 33) = 1.0000
• For the second row, we repeat the same process.

## 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.