SAP HANA Predictive Analysis Library (PAL)

Expected Result
3.6.10 Scaling Range
In real world scenarios the collected continuous attributes are usually distributed within different ranges. It is a
common practice to have the data well scaled so that data mining algorithms like neural networks, nearest
neighbor classification and clustering can give more reliable results.
This release of PAL provides three scaling range methods described below. In the following, X ip and Y ip are the
original value and transformed value of the i-th record and p-th attribute, respectively.
1. Min-Max Normalization
Each transformed value is within the range [new_minA, new_maxA], where new_minA and new_maxA are
use-specified parameters. Supposing that minA and maxA are the minimum and maximum values of
attribute A, we get the following calculation formula:
Y ip = (X ip ‒ minA) × (new_maxA - new_minA) / (maxA - minA) + new_minA
2. Z-Score Normalization (or zero-mean normalization).
PAL uses three z-score methods.
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Mean-Standard Deviation
The transformed values have mean 0 and standard deviation 1. The transformation is made as follows:
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Where μ p and σ p are mean and standard deviations of the original values of the p-th attributes.
Mean-Mean Absolute Deviation
SAP HANA Predictive Analysis Library (PAL)
PAL Functions P U B L I C 479