SAP HANA Predictive Analysis Library (PAL)

If an attribute is of category type, it will be converted to a binary vector and then be used as a numerical
attribute. For example, in the below table, "Gender" is of category type.
Table 56:
Customer ID Age Income Gender
T1 31 10,000 Female
T2 27 8,000 Male
Because "Gender" has two distinct values, it will be converted into a binary vector with two dimensions:
Table 57:
Customer ID Age Income Gender_1 Gender_2
T1 31 10,000 0 1
T2 27 8,000 1 0
Thus, the Euclidean distance between T1 and T2 is:
Where γ is the weight to be given to the transposed categorical attributes to lessen the impact on the
clustering from the 0/1 attributes. Then you can use the traditional method to update the medoid of every
cluster.
Prerequisites
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The input data contains an ID column and the other columns are of integer, varchar, nvarchar, or double
data type.
The input data does not contain null value. The algorithm will issue errors when encountering null values.
KMEDOIDS
This is a clustering function using the K-medoids algorithm.
Procedure Generation
CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE (‘AFLPAL’, ‘KMEDOIDS’,
‘’, '', );
The signature table should contain the following records:
Table 58:
Position Schema Name Table Type Name Parameter Type
1 IN
2 IN
84 P U B L I C
SAP HANA Predictive Analysis Library (PAL)
PAL Functions