# 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
20Â Â

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

## 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 exactÂ same scale.

## Implementation of Min-Max normalization in C++

• Calculate and show the maximum value from the array.
• Calculate and show the minimum value from the array.
• Calculate and show the average value from the array, and the number of values that are larger than the average.
• Calculate and show the normalized values of the original array values.

Output

Example ofÂ Min-max scaling in data mining:Â

Min-maxÂ normalization detail is available in the previous tutorial.

Here, There is just another example of the practice.

## Min Max Normalization in Python and Matlab

Min-Max normalization is explained very briefly in the next tutorial.

## Video Lecture

FAQ

min-max normalization python. min-max normalization in r. min max normalization pandas
min max normalization excel. min-max normalization vs standardization. min-max normalization matlab. use min-max normalization to transform the value 35 for age on the range.