OWDBSCAN: Documentation

doc/visual-programming/source/index.rst

@@ -122,10 +122,11 @@ Unsupervised
    widgets/unsupervised/hierarchicalclustering
    widgets/unsupervised/kmeans
    widgets/unsupervised/louvainclustering
+   widgets/unsupervised/DBSCAN
    widgets/unsupervised/mds
    widgets/unsupervised/tsne
    widgets/unsupervised/manifoldlearning
-   
+
 
 Evaluation
 ----------

doc/visual-programming/source/widgets/unsupervised/DBSCAN.md

@@ -0,0 +1,46 @@
+DBSCAN
+======
+
+Groups items using the DBSCAN clustering algorithm.
+
+**Inputs**
+
+- Data: input dataset
+
+**Outputs**
+
+- Data: dataset with cluster index as a class attribute
+
+The widget applies the
+[DBSCAN clustering](https://en.wikipedia.org/wiki/DBSCAN) 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
+[methods article](https://www.aaai.org/Papers/KDD/1996/KDD96-037.pdf).
+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.
+
+![](images/dbscan-stamped.png)
+
+1. Set *minimal number of core neighbors* for a cluster and *maximal
+neighborhood distance.
+2. Set the distance metric that is used in grouping the items.
+3. If *Apply Automatically* is ticked, the widget will commit changes
+automatically. Alternatively, click *Apply*.
+4. 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**.
+
+Example
+-------
+
+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.
+
+![](images/dbscan-example.png)