Github user viirya commented on a diff in the pull request:
https://github.com/apache/spark/pull/21119#discussion_r184874390
--- Diff: python/pyspark/ml/clustering.py ---
@@ -1156,6 +1156,205 @@ def getKeepLastCheckpoint(self):
return self.getOrDefault(self.keepLastCheckpoint)
+@inherit_doc
+class PowerIterationClustering(HasMaxIter, HasPredictionCol,
JavaTransformer, JavaParams,
+ JavaMLReadable, JavaMLWritable):
+ """
+ .. note:: Experimental
+
+ Power Iteration Clustering (PIC), a scalable graph clustering
algorithm developed by
+ <a href=http://www.icml2010.org/papers/387.pdf>Lin and Cohen</a>. From
the abstract:
+ PIC finds a very low-dimensional embedding of a dataset using
truncated power
+ iteration on a normalized pair-wise similarity matrix of the data.
+
+ PIC takes an affinity matrix between items (or vertices) as input. An
affinity matrix
+ is a symmetric matrix whose entries are non-negative similarities
between items.
+ PIC takes this matrix (or graph) as an adjacency matrix.
Specifically, each input row
+ includes:
+
+ - :py:attr:`idCol`: vertex ID
+ - :py:attr:`neighborsCol`: neighbors of vertex in :py:attr:`idCol`
+ - :py:attr:`similaritiesCol`: non-negative weights (similarities) of
edges between the
+ vertex in :py:attr:`idCol` and each neighbor in
:py:attr:`neighborsCol`
+
+ PIC returns a cluster assignment for each input vertex. It appends a
new column
+ :py:attr:`predictionCol` containing the cluster assignment in
:py:attr:`[0,k)` for
+ each row (vertex).
+
+ .. note::
+
+ - [[PowerIterationClustering]] is a transformer with an expensive
[[transform]] operation.
+ Transform runs the iterative PIC algorithm to cluster the whole
input dataset.
+ - Input validation: This validates that similarities are non-negative
but does NOT validate
+ that the input matrix is symmetric.
+
+ .. seealso:: <a href=http://en.wikipedia.org/wiki/Spectral_clustering>
+ Spectral clustering (Wikipedia)</a>
--- End diff --
You can check other places using `seealso`:
```python
.. seealso:: `Spectral clustering \
<http://en.wikipedia.org/wiki/Spectral_clustering>`_
```
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