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diff --git a/docs/img/PIClusteringFiveCirclesInputsAndOutputs.png b/docs/img/PIClusteringFiveCirclesInputsAndOutputs.png Binary files differnew file mode 100644 index 0000000000..ed9adad11d --- /dev/null +++ b/docs/img/PIClusteringFiveCirclesInputsAndOutputs.png diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md index c696ae9c8e..413b824e36 100644 --- a/docs/mllib-clustering.md +++ b/docs/mllib-clustering.md @@ -34,6 +34,26 @@ a given dataset, the algorithm returns the best clustering result). * *initializationSteps* determines the number of steps in the k-means\|\| algorithm. * *epsilon* determines the distance threshold within which we consider k-means to have converged. +### Power Iteration Clustering + +Power iteration clustering is a scalable and efficient algorithm for clustering points given pointwise mutual affinity values. Internally the algorithm: + +* accepts a [Graph](https://spark.apache.org/docs/0.9.2/api/graphx/index.html#org.apache.spark.graphx.Graph) that represents a normalized pairwise affinity between all input points. +* calculates the principal eigenvalue and eigenvector +* Clusters each of the input points according to their principal eigenvector component value + +Details of this algorithm are found within [Power Iteration Clustering, Lin and Cohen]{www.icml2010.org/papers/387.pdf} + +Example outputs for a dataset inspired by the paper - but with five clusters instead of three- have he following output from our implementation: + +<p style="text-align: center;"> + <img src="img/PIClusteringFiveCirclesInputsAndOutputs.png" + title="The Property Graph" + alt="The Property Graph" + width="50%" /> + <!-- Images are downsized intentionally to improve quality on retina displays --> +</p> + ### Examples <div class="codetabs"> |
