SAP HANA Predictive Analysis Library (PAL)

4 End-to-End Scenarios
This section provides end-to-end scenarios of predictive analysis with PAL algorithms.
4.1 Scenario: Predict Segmentation of New Customers for
a Supermarket
We wish to predict segmentation/clustering of new customers for a supermarket. First use the K-means
function in PAL to perform segmentation/clustering for existing customers in the supermarket. The output
can then be used as the training data for the C4.5 Decision Tree function to predict new customers’
segmentation/clustering.
Technology Background
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K-means clustering is a method of cluster analysis whereby the algorithm partitions N observations or
records into K clusters, in which each observation belongs to the cluster with the nearest center. It is one
of the most commonly used algorithms in clustering method.
Decision trees are powerful and popular tools for classification and prediction. Decision tree learning, used
in statistics, data mining, and machine learning uses a decision tree as a predictive model which maps the
observations about an item to the conclusions about the item's target value.
Implementation Steps
Assume that:
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DM_PAL is a schema belonging to USER1; and
USER1 has been assigned the AFLPM_CREATOR_ERASER_EXECUTE role; and
USER1 has been assigned the AFL__SYS_AFL_AFLPAL_EXECUTE or
AFL__SYS_AFL_AFLPAL_EXECUTE_WITH_GRANT_OPTION role.
Step 1
Input customer data and use the K-means function to partition the data set into K clusters. In this example,
nine rows of data will be input. K equals 3, which means the customers will be partitioned into three levels.
SET SCHEMA DM_PAL;
DROP TYPE PAL_KMEANS_RESASSIGN_T;
CREATE TYPE PAL_KMEANS_RESASSIGN_T AS TABLE(
"ID" INT,
"CENTER_ASSIGN" INT,
552 P U B L I C
SAP HANA Predictive Analysis Library (PAL)
End-to-End Scenarios