Question

Feature scaling is an important step before applying the K-Mean algorithm. What is the reason behind this?

Select the correct answer from below options: 

A. In distance calculation, it will give the same weights for all features

B. You always get the same clusters. If you use or don’t use feature scaling

C. In Manhattan distance, it is an important step, but in Euclidean distance, it is not

D. None of these

Answer 1

Answer: (A)

Feature scaling ensures that all the features get the same weight in the clustering analysis. Consider a scenario of clustering people based on their weights (in KG) with a range of 55-110 and height (in inches) with a range of 5.6 to 6.4. In this case, the clusters produced without scaling can be very misleading as the range of weight is much higher than that of height. Therefore, it is necessary to bring them to the same scale so that they have equal weightage on the clustering result.