diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md
index 7978e934fb36b..d0aa1a8bb0678 100644
--- a/docs/mllib-clustering.md
+++ b/docs/mllib-clustering.md
@@ -34,7 +34,7 @@ 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. 
 
-## Examples
+### Examples
 
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
@@ -153,3 +153,75 @@ provided in the [Self-Contained Applications](quick-start.html#self-contained-ap
 section of the Spark
 Quick Start guide. Be sure to also include *spark-mllib* to your build file as
 a dependency.
+
+## Streaming clustering
+
+When data arrive in a stream, we may want to estimate clusters dynamically, updating them as new data arrive. MLlib provides support for streaming KMeans clustering, with parameters to control the decay (or "forgetfulness") of the estimates. The algorithm uses a generalization of the mini-batch KMeans update rule. For each batch of data, we assign all points to their nearest cluster, compute new cluster centers, then update each cluster using:
+
+`\begin{equation}
+    c_{t+1} = \frac{c_tn_t\alpha + x_tm_t}{n_t\alpha+m_t}
+\end{equation}`
+`\begin{equation}
+    n_{t+1} = n_t + m_t  
+\end{equation}`
+
+Where `$c_t$` is the previous center for the cluster, `$n_t$` is the number of points assigned to the cluster thus far, `$x_t$` is the new cluster center from the current batch, and `$m_t$` is the number of points added to the cluster in the current batch. The decay factor `$\alpha$` can be used to ignore the past: with `$\alpha$=1` all data will be used from the beginning; with `$\alpha$=0` only the most recent data will be used. This is analogous to an expontentially-weighted moving average.
+
+### Examples
+
+This example shows how to estimate clusters on streaming data.
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+
+First we import the neccessary classes.
+
+{% highlight scala %}
+
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.clustering.StreamingKMeans
+
+{% endhighlight %}
+
+Then we make an input stream of vectors for training, as well as one for testing. We assume a StreamingContext `ssc` has been created, see [Spark Streaming Programming Guide](streaming-programming-guide.html#initializing) for more info. For this example, we use vector data. 
+
+{% highlight scala %}
+
+val trainingData = ssc.textFileStream("/training/data/dir").map(Vectors.parse)
+val testData = ssc.textFileStream("/testing/data/dir").map(Vectors.parse)
+
+{% endhighlight %}
+
+We create a model with random clusters and specify the number of clusters to find
+
+{% highlight scala %}
+
+val numDimensions = 3
+val numClusters = 2
+val model = new StreamingKMeans()
+    .setK(numClusters)
+    .setDecayFactor(1.0)
+    .setRandomWeights(numDimensions)
+
+{% endhighlight %}
+
+Now register the streams for training and testing and start the job, printing the predicted cluster assignments on new data points as they arrive.
+
+{% highlight scala %}
+
+model.trainOn(trainingData)
+model.predictOn(testData).print()
+
+ssc.start()
+ssc.awaitTermination()
+ 
+{% endhighlight %}
+
+As you add new text files with data the cluster centers will update. Each data point should be formatted as `[x1, x2, x3]`. Anytime a text file is placed in `/training/data/dir` 
+the model will update. Anytime a text file is placed in `/testing/data/dir` you will see predictions. With new data, the cluster centers will change.
+
+
+</div>
+
+</div>