When performing K$-$means Clustering, it is considered vital to scale the variables before clustering. This is done primarily to:

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make the model less susceptible to outliers

convert all the features to a comparable scale so as to give each of them equal importance

avoid multicollinearity among the variables

treat missing values to make the data more robust for further analysis