SAP HANA Predictive Analysis Library (PAL)

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 mean of every
cluster. Assuming one cluster only has T1 and T2, the mean is:
Table 36:
Customer ID Age Income Gender_1 Gender_2
Center1 29.0 9000.0 0.5 0.5
The means of categorical attributes will not be outputted. Instead, the means will be replaced by the modes
similar to the K-Modes algorithm. Take the below center for example:
Table 37:
Age Income Gender_1 Gender_2
Center 29.0 9000.0 0.25 0.75
Because "Gender_2" is the maximum value, the output will be:
Table 38:
Age Income Gender
Center 29.0 9000.0 Female
Prerequisites
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The input data contains an ID column and the other columns are of integer or double data type.
The input data does not contain null value. The algorithm will issue errors when encountering null values.
KMEANS
This is a clustering function using the k-means algorithm.
Procedure Generation
CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE (‘AFLPAL’, ‘KMEANS’, ‘’,
'', );
64 P U B L I C
SAP HANA Predictive Analysis Library (PAL)
PAL Functions