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
marks |
marks after Min-Max normalization |
8 | 0 |
10 | 0.16 |
15 | 0.58 |
20 | 1 |
Min max normalization example
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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. |