DBSCAN

Kloudfuse provides Density-based spatial clustering of applications with noise (DBSCAN) implementation of Outlier Detection.

In DBSCAN, the tolerance level, or eps, determines the clustering radius of the neighborhood around each point. The eps controls the sensitivity of outlier detection. A lower tolerance detects more subtle outliers, while a higher tolerance detects only the most significant deviations.

In Dashboards

To use the dbscan in a dashboard, apply the following function:

dbscan( \
  ${promql}, \ (1)
  ${tolerance}, \ (2)
  1, 1 \
)
code
1 ${promql}: PromQL query to evaluate
2 ${tolerance}: The sensitivity of DBSCAN; value range between 0.33 and 5.0, inclusive. A lower value is more sensitive to deviations.

Limitations

The alert rule evaluates all data points pulled by the query. When used in a dashboard, evaluates the whole data collection, not individual time slices. If a time series triggers an alert, then the whole time range is in active alerting state. This presents differently from other alert functions used in the dashboard.

Next steps

For an in-depth discussion of the DBSCAN algorithm, see these external resources: