SAP HANA Predictive Analysis Library (PAL)

PAL_KMEDIANS_ASSIGN_TBL:
3.1.9 K-Medoids
This is a clustering algorithm related to the K-means algorithm. Both k-medoids and k-means algorithms
partition n observations into k clusters in which each observation is assigned to the cluster with the closest
center. In contrast to k-means algorithm, k-medoids clustering does not calculate means, but medoids to be
the new cluster centers.
A medoid is defined as the center of a cluster, whose average dissimilarity to all the objects in the cluster is
minimal. Compared with the K-means algorithm, K-medoids is more robust to noise and outliers.
Given an initial set of K medoids: m1, ..., mk, the algorithm proceeds by alternating between two steps as
below:
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Assignment step: assigns each observation to the cluster with the closest center.
Update step: calculates the new medoid to be the center of the observations for each cluster.
The algorithm repeats until the assignments no longer change.
In PAL, the K-medoids algorithm supports multi-threads, data normalization, different distance level
measurements, and cluster quality measurement (Silhouette). It 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
SAP HANA Predictive Analysis Library (PAL)
PAL Functions P U B L I C 83