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Normalization consists of transforming numerical values into a specific range, such as [–1.0,1.0] or [0.0,1.0] such that x_new = (x_old-shift)/scale
. Normalization usually results in values whose absolute value is less than or equal to 1.0. Normalization applies only to numerical columns.
You can normalize numerical attributes only.
To normalize an column, select Normalization for Transformation type and select a Normalization Type.
Data Miner supports these normalization types:
Min Max: Normalize the column using the transformation x_new = (x_old-min)/ (max-min)
. min-max is the default normalization type.
Z-score: Normalize numerical columns using the mean and standard deviation computed from the data. Normalize each column using the transformation x_new = (x-mean)/standard deviation
.
Linear Scale: Normalize each column using the transformation x_new = (x-0)/ max(abs(max), abs(min))
manual: Define normalization by specifying shift and scale for the transformation x_new = (x_old-shift)/scale. You must specify shift and scale.
Select the normalization type, specify any required values, and then click OK.