Github user freeman-lab commented on a diff in the pull request:
https://github.com/apache/spark/pull/2906#discussion_r22634674
--- Diff: docs/mllib-clustering.md ---
@@ -154,6 +156,175 @@ section of the Spark
Quick Start guide. Be sure to also include *spark-mllib* to your build
file as
a dependency.
+
+### Hierarchical Clustering
+
+MLlib supports
+[hierarchical
clustering](http://en.wikipedia.org/wiki/Hierarchical_clustering), one of the
most commonly used clustering algorithm which seeks to build a hierarchy of
clusters.
+Strategies for hierarchical clustering generally fall into two types.
+One is the agglomerative clustering which is a "bottom up" approach: each
observation starts in its own cluster, and pairs of clusters are merged as one
moves up the hierarchy.
+The other is the divisive clustering which is a "top down" approach: all
observations start in one cluster, and splits are performed recursively as one
moves down the hierarchy.
+The MLlib implementation only includes a divisive hierarchical clustering
algorithm.
+
+The implementation in MLlib has the following parameters:
+
+* *k* is the number of maximum desired clusters.
+* *subIterations* is the maximum number of iterations to split a cluster
to its 2 sub clusters.
+* *numRetries* is the maximum number of retries if a splitting doesn't
work as expected.
+* *epsilon* determines the saturate threshold to consider the splitting to
have converged.
+
+
+
+### Hierarchical Clustering Example
+
+<div class="codetabs">
+
+<div data-lang="scala" markdown="1">
+The following code snippets can be executed in `spark-shell`.
+
+In the following example after loading and parsing data,
+we use the hierarchical clustering object to cluster the sample data into
three clusters.
--- End diff --
Clarify that this means three clusters at the bottom-most levels of a
hierarchical tree.
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