# DBSCAN¶

Groups items using the DBSCAN clustering algorithm.

**Inputs**

- Data: input dataset

**Outputs**

- Data: dataset with cluster label as a meta attribute

The widget applies the DBSCAN clustering algorithm to the data and outputs a new dataset with cluster labels as a meta attribute. The widget also shows the sorted graph with distances to k-th nearest neighbors. With k values set to **Core point neighbors** as suggested in the methods article. This gives the user the idea of an ideal selection for **Neighborhood distance** setting. As suggested by the authors, this parameter should be set to the first value in the first “valley” in the graph.

**Parameters**:*Core point neighbors*: The number of neighbors for a point to be considered as a core point.*Neighborhood distance*: The maximum distance between two samples for one to be considered as in the neighborhood of the other.

- Distance metric used in grouping the items (Euclidean, Manhattan, or Cosine). If
*Normalize features*is selected, the data will be standardized column-wise (centered to mean and scaled to standard deviation of 1). - If
*Apply Automatically*is ticked, the widget will commit changes automatically. Alternatively, click*Apply*.

The graph shows the distance to the k-th nearest neighbor. *k* is
set by the **Core point neighbor** option. With moving the black slider
left and right you can select the right **Neighborhood distance**.

## Example¶

In the following example, we connected the File widget with the Iris dataset to the DBSCAN widget. In the DBSCAN widget, we set **Core points neighbors** parameter to 5. And select the **Neighborhood distance** to the value in the first “valley” in the graph. We show clusters in the Scatter Plot widget.