SAP HANA Predictive Analysis Library (PAL)

CREATE COLUMN TABLE PAL_KMEANS_CENTERS_TBL LIKE PAL_KMEANS_CENTERS_T;
CALL DM_PAL.PAL_KMEANS_PROC(PAL_KMEANS_DATA_TBL, "#PAL_CONTROL_TBL",
PAL_KMEANS_RESASSIGN_TBL, PAL_KMEANS_CENTERS_TBL) WITH OVERVIEW;
SELECT * FROM PAL_KMEANS_CENTERS_TBL;
SELECT * FROM PAL_KMEANS_RESASSIGN_TBL;
DROP TABLE PAL_KMEANS_RESULT_TBL;
CREATE COLUMN TABLE PAL_KMEANS_RESULT_TBL(
"AGE" DOUBLE,
"INCOME" DOUBLE,
"LEVEL" INT
);
TRUNCATE TABLE PAL_KMEANS_RESULT_TBL;
INSERT INTO PAL_KMEANS_RESULT_TBL(
SELECT PAL_KMEANS_DATA_TBL.AGE, PAL_KMEANS_DATA_TBL.INCOME,
PAL_KMEANS_RESASSIGN_TBL.CENTER_ASSIGN
FROM PAL_KMEANS_RESASSIGN_TBL
INNER JOIN PAL_KMEANS_DATA_TBL
ON PAL_KMEANS_RESASSIGN_TBL.ID = PAL_KMEANS_DATA_TBL.ID
);
SELECT * FROM PAL_KMEANS_RESULT_TBL;
The result should show the following in PAL_KMEANS_RESULT_TBL:
Step 2
Use the above output as the training data of C4.5 Decision Tree. The C4.5 Decision Tree function will generate
a tree model which maps the observations about an item to the conclusions about the item's target value.
SET SCHEMA DM_PAL;
DROP TYPE PAL_CDT_DATA_T;
CREATE TYPE PAL_CDT_DATA_T AS TABLE(
"AGE" DOUBLE,
"INCOME" DOUBLE,
"LEVEL" INT
);
DROP TYPE PAL_CDT_JSONMODEL_T;
CREATE TYPE PAL_CDT_JSONMODEL_T AS TABLE(
"ID" INT,
"JSONMODEL" VARCHAR(5000)
);
DROP TYPE PAL_CDT_PMMLMODEL_T;
CREATE TYPE PAL_CDT_PMMLMODEL_T AS TABLE(
"ID" INT,
"PMMLMODEL" VARCHAR(5000)
);
DROP TYPE PAL_CONTROL_T;
CREATE TYPE PAL_CONTROL_T AS TABLE(
"NAME" VARCHAR (100),
"INTARGS" INTEGER,
"DOUBLEARGS" DOUBLE,
554 P U B L I C
SAP HANA Predictive Analysis Library (PAL)
End-to-End Scenarios