Properties that can be configured for the HANA K-Means algorithm.
| Property | Description |
|---|---|
| Output Mode | Select the mode in which you want to use the output of this algorithm. |
| Features | Select the input columns with which you want to perform the analysis. |
| Category Columns | Select the input columns, which you want to consider as category columns. |
| Categorical Weights | Enter the categorical weights. |
| Calculate Silhouette | Select this option to calculate silhouette values. Silhouette signifies the quality of clustering. The silhouette value 1 signifies that the clustering is good and 0 signifies that the clustering is bad. |
| Missing Values | Select the method for handling missing values. Possible
methods:
|
| 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 the cluster name. |
| Distance | Enter a name for the newly created column that contains the distance of the clusters from their centroids' name. |
| Maximum Iterations | Enter the number of iterations allowed for finding clusters. The default value is 100. |
| Center Calculation Method | Select the method to be used for calculating initial cluster centers. |
| Distance Measure | Enter the method for calculating the distance between the item and cluster centre. |
| Normalization Type | Select the type of normalization. |
| Number of Threads | Enter the number of threads that can be used for execution. The default value is 1. |
| Exit Threshold | Enter the threshold value for exiting from the iterations. The default value is 0.000000001. |