SAP HANA Predictive Analysis Library (PAL)

PAL_GMM_RESULTSMODEL_TBL:
3.1.7 K-Means
In predictive analysis, k-means clustering is a method of cluster analysis. The k-means algorithm partitions n
observations or records into k clusters in which each observation belongs to the cluster with the nearest
center. In marketing and customer relationship management areas, this algorithm uses customer data to
track customer behavior and create strategic business initiatives. Organizations can thus divide their
customers into segments based on variants such as demography, customer behavior, customer profitability,
measure of risk, and lifetime value of a customer or retention probability.
Clustering works to group records together according to an algorithm or mathematical formula that attempts
to find centroids, or centers, around which similar records gravitate. The most common algorithm uses an
iterative refinement technique. It is also referred to as Lloyd's algorithm:
Given an initial set of k means m1, ..., mk, the algorithm proceeds by alternating between two steps:
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Assignment step: assigns each observation to the cluster with the closest mean.
Update step: calculates the new means to be the center of the observations in the cluster.
The algorithm repeats until the assignments no longer change.
The k-means implementation in PAL supports multi-thread, data normalization, different distance level
measurement, and cluster quality measurement (Silhouette). The implementation does not support
categorical data, but this can be managed through data transformation. The first K and random K starting
methods are supported.
Support for Categorical Attributes
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 34:
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 35:
Customer ID Age Income Gender_1 Gender_2
T1 31 10,000 0 1
T2 27 8,000 1 0
SAP HANA Predictive Analysis Library (PAL)
PAL Functions P U B L I C 63