# DBSCAN

Kloudfuse provides Density-based spatial clustering of applications with noise (DBSCAN) implementation of [Outlier Detection](https://docs.kloudfuse.com/platform/latest/query-languages/fuseql/algorithm/).

## Parameters

tolerance

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.

## How it works

DBSCAN groups the input series by similarity: series whose behavior falls within a small neighborhood of each other form dense clusters, and clusters grow outward through chains of neighboring series. Series that end up in no cluster are the outliers — fleet members behaving unlike their peers. The `tolerance` parameter scales the neighborhood size, so lower values flag smaller deviations as outliers.

## In Dashboards

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

```none
dbscan( 
  ${promql}, 
  ${tolerance}, 
  1, 1 
)
```

|     |     |
| --- | --- |
| **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. |

For the operator reference — syntax, parameters, and a validated example — see [dbscan](https://docs.kloudfuse.com/platform/latest/query-languages/promql/algorithm/#dbscan) in the PromQL documentation.

## Limitations

- The alert rule evaluates all data points pulled by the query. When used in a dashboard, it 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.

- DBSCAN clusters the **set of input series** against each other, so it needs enough peer series to form a meaningful cluster — applying it to one or two series cannot distinguish an outlier from a cluster.

- Conversely, very wide inputs (hundreds of series) increase comparison cost; group to the fleet dimension you actually want to compare, such as one series per pod or per service.

## Next steps

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

- [DBSCAN Key Concepts and Parameters](https://www.datacamp.com/tutorial/dbscan-clustering-algorithm.html) in DataCamp Tutorials
- [DBSCAN](https://en.wikipedia.org/wiki/DBSCAN) in Wikipedia