HANA R-K-Means

Properties that can be configured for the HANA R-K-Means algorithm.

Syntax Use this algorithm to cluster observations into groups of related observations without any prior knowledge of those relationships. The algorithm clusters observations into k groups, where k is provided as an input parameter. The algorithm then assigns each observation to clusters based on the proximity of the observation to the mean of the cluster. The process continues until the clusters converge.
Note
  • You might obtain a different cluster number for each cluster each time you execute the HANA R-K-Means algorithm. However, the observations in each cluster remain the same.
  • Creating models using the HANA R-K-Means algorithm is not supported.
HANA R-K-Means Properties
Table 1: HANA R-K-Means Properties
Property Description
Output Mode Select the mode in which you want to use the output of this algorithm
Features Select input columns with which you want to perform the analysis.
Number of Clusters Enter the number of groups for clustering. The default value is 5.
Cluster Name Enter a name for the newly created column that contains cluster numbers.
Maximum Iterations Enter the number of iterations allowed for finding clusters. The default value is 100.
Number of Initial Cluster Center Sets Enter the number of random initial cluster center sets for clustering (n start). The default value is 1.
Initial Cluster Center Seed Enter a value to randomly select initial cluster centers from acquired data.
Algorithm Type Select the type of algorithm that you want to use for performing HANA R-K-Means clustering.