HANA Anomaly Detection

Properties that can be configured for the HANA Anomaly Detection algorithm.

Syntax Use this algorithm to find patterns in data that do not conform to expected behavior.
Note Creating models using the HANA Anomaly Detection algorithm is not supported.
HANA Anomaly Detection Properties
Table 1: Algorithm Properties
Property Description
Output Mode Select the mode in which you want to use the output of this algorithm.
Independent Columns Select the input source columns.
Missing Values Select the method for handling missing values.
Possible values:
  • Ignore: The algorithm skips the records containing missing values in the independent or dependent columns.
  • Keep: The algorithm retains the records containing missing values during calculation.
Percentage of Anomalies Enter the percentage value that indicates the proportion of anomalies in the source data. The default value is 10.
Anomaly Detection Method Select the anomaly detection method.
  • By distance from the center
  • By sum of distances from all centers
Maximum Iterations Enter the number of iterations allowed for finding clusters. The default value is 100.
Center Calculation Method Select the method to use for calculating the initial cluster centers.
Normalization Type Select the type of normalization.
Number of Clusters Enter the number of groups for clustering.
Number of Threads Enter the number of threads that the algorithm should use during execution. The default value is 1.
Exit Threshold Enter the threshold value for exiting from the iterations. The default value is 0.0001.
Distance Measure Enter the measure for calculating the distance between the records and cluster centers.
Predicted Column Name Enter a name for the new column that contains the predicted values.