Since variables with large variances tend to have more effect on the distance measure than those with small variances, it is recommended that you standardize the variables before the computation of the distance measure.Available methods for standardization are described in the table below:
Standardizes values to zero (0) with a scale equivalent to the sum of the values. Standardizes values to zero (0) with a scale equivalent to the Euclidean length. Standardizes values to zero (0) with a scale equivalent to the standard deviation about the origin. Standardizes values to zero (0) with a scale equivalent to the maximum absolute value. Standardizes values to the biweight 1-step M -estimate with a scale equivalent to the biweight A -estimate.Note : 4.685 is the default numeric tuning constant used in this method. Standardizes values to the Huber 1-step M -estimate with a scale equivalent to the Huber A -estimate.Note : 1.345 is the default numeric tuning constant used in this method. Note : 1 is the default numeric tuning constant used in this method. Note : 0.1 is the default numeric constant giving the proportion of pairs to be included in the estimation of the within-cluster variances. Note : 0.1 is the default numeric constant giving the proportion of data to be contained in the spacing.
Select the desired standardization method using the drop-down menu.Note : Select the blank space to apply no standardization to your variables.Refer to the SAS PROC STDIZE documentation for other options and details.