Groups items using the DBSCAN clustering algorithm.
- Data: dataset with cluster index as a class attribute
The widget applies the
DBSCAN clustering algorithm to
the data and outputs a new dataset with cluster indices 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
This gives the user the idea of an
ideal selection for Neighborhood distance setting. As suggested by
authors this parameter should be set to the first value in the first
“valley” in the graph.
- Set minimal number of core neighbors for a cluster and *maximal
- Set the distance metric that is used in grouping the items.
- 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 Neighbourhood distance.
In the following example, we connected the File widget with selected
Iris dataset to the DBSCAN widget. In the DBSCAN widget, we set
Core points neighbors parameter to 5. And select the
Neighbourhood distance to the value in the first “valley” in the
graph. We show clusters in the Scatter Plot widget.