[ENH] Move Louvain clustering from prototypes to core

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

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+Louvain Clustering
+=======
+
+Groups items using the Louvain clustering algorithm.
+
+Inputs
+    Data
+        input dataset
+
+Outputs
+    Data
+        dataset with cluster index as a class attribute
+    Graph (with the Network addon)
+		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>`_ community 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.
+
+
+.. figure:: images/Louvain-stamped.png
+
+1. PCA processing is typically be applied to the original data to remove noise.
+2. The distance metric is used for finding specified number of nearest
+   neighbors.
+3. The number of nearest neighbors to use to form the KNN graph.
+4. 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.
+5. When *Apply Automatically* is ticked, the widget will automatically
+   communicate all changes. Alternatively, click *Apply*.
+
+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).