OWLouvain: Add documentation stub

doc/visual-programming/source/widgets/unsupervised/louvainclustering.rst

@@ -0,0 +1,41 @@
+k-Means
+=======
+
+Groups items using the k-Means clustering algorithm.
+
+Inputs
+    Data
+        input dataset
+
+Outputs
+    Data
+        dataset with cluster index as a class attribute
+	Graph (if the Network addon is installed)
+		the weighted k-nearest neighbor graph
+
+
+The widget first converts the input data into a k-nearest neighbor graph. To
+preserve the notions of distance, the Jaccard index for the number of shared
+ neighbors is used to weight the edges. Finally, a
+`modularity optimization <https://en.wikipedia.org/wiki/Louvain_Modularity>`_
+ communtiy detection algorithm is applied to the graph to retrieve clusters of
+highly interconnected nodes. The widget outputs a new dataset in which the
+cluster index is used as a meta attribute.
+
+Parameters
+----------
+
+- PCA processing is typically be applied to the original data to remove noise.
+- The distance metric is used for finding specified number of nearest
+	neighbors. The nearest neighbors form a nearest neighbor graph.
+- Resolution is a parameter for the Louvain community detection algorithm that
+	affects the size of the recovered clusters. Smaller resolutions recover
+	smaller, and therefore a larger number of clusters, and conversely, larger
+	values recover clusters containing more data points.
+
+References
+----------
+
+Blondel, Vincent D., et al. "Fast unfolding of communities in large networks." Journal of statistical mechanics: theory and experiment 2008.10 (2008): P10008.
+
+Lambiotte, Renaud, J-C. Delvenne, and Mauricio Barahona. "Laplacian dynamics and multiscale modular structure in networks." arXiv preprint arXiv:0812.1770 (2008).