Parameters | Pattern Discovery | Standardization Method

Standardization Method
Tip: 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:
 Method Description STD Standardizes values to the mean with a scale equivalent to the standard deviation. MEAN MEDIAN Standardizes values to the median with a scale equivalent to one (1). SUM Standardizes values to zero (0) with a scale equivalent to the sum of the values. EUCLEN Standardizes values to zero (0) with a scale equivalent to the Euclidean length. USTD Standardizes values to zero (0) with a scale equivalent to the standard deviation about the origin. RANGE Standardizes values to the minimal value with a scale equivalent to the range of the values. MIDRANGE MAXABS Standardizes values to zero (0) with a scale equivalent to the maximum absolute value. IQR Standardizes values to the median with a scale equivalent to the interquartile range. MAD Standardizes values to the median with a scale equivalent to the mean absolute deviation from the median. ABW(4.685) 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. AHUBER(1.345) 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. AWAVE(1) Note: 1 is the default numeric tuning constant used in this method. AGK(0.1) Standardizes values to the mean with a scale equivalent to the AGK estimate. Note: 0.1 is the default numeric constant giving the proportion of pairs to be included in the estimation of the within-cluster variances. SPACING(0.1) Standardizes values to the mid-minimum spacing with a scale equivalent to the minimum spacing. Note: 0.1 is the default numeric constant giving the proportion of data to be contained in the spacing. L(1) Standardizes values to the L(1) value with a scale equivalent to the L(1) value. Note: 0.1 is the default numeric constant specifying the power to which differences are to be raised in computing an L(p) or Minkowski metric.
To Specify a Standardization Method:
Tip: Select the blank space to apply no standardization to your variables.