From a2dce22e0a25922e2052318d32f32877b7c27ec2 Mon Sep 17 00:00:00 2001
From: Xiangrui Meng <meng@databricks.com>
Date: Fri, 20 Nov 2015 16:51:47 -0800
Subject: Revert "[SPARK-11689][ML] Add user guide and example code for LDA
 under spark.ml"

This reverts commit e359d5dcf5bd300213054ebeae9fe75c4f7eb9e7.
---
 docs/ml-clustering.md | 30 ------------------------------
 docs/ml-guide.md      |  3 +--
 docs/mllib-guide.md   |  1 -
 3 files changed, 1 insertion(+), 33 deletions(-)
 delete mode 100644 docs/ml-clustering.md

(limited to 'docs')

diff --git a/docs/ml-clustering.md b/docs/ml-clustering.md
deleted file mode 100644
index 1743ef43a6..0000000000
--- a/docs/ml-clustering.md
+++ /dev/null
@@ -1,30 +0,0 @@
----
-layout: global
-title: Clustering - ML
-displayTitle: <a href="ml-guide.html">ML</a> - Clustering
----
-
-In this section, we introduce the pipeline API for [clustering in mllib](mllib-clustering.html).
-
-## Latent Dirichlet allocation (LDA)
-
-`LDA` is implemented as an `Estimator` that supports both `EMLDAOptimizer` and `OnlineLDAOptimizer`,
-and generates a `LDAModel` as the base models. Expert users may cast a `LDAModel` generated by
-`EMLDAOptimizer` to a `DistributedLDAModel` if needed.
-
-<div class="codetabs">
-
-Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.clustering.LDA) for more details.
-
-<div data-lang="scala" markdown="1">
-{% include_example scala/org/apache/spark/examples/ml/LDAExample.scala %}
-</div>
-
-<div data-lang="java" markdown="1">
-
-Refer to the [Java API docs](api/java/org/apache/spark/ml/clustering/LDA.html) for more details.
-
-{% include_example java/org/apache/spark/examples/ml/JavaLDAExample.java %}
-</div>
-
-</div>
\ No newline at end of file
diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index 6f35b30c3d..be18a05361 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -40,7 +40,6 @@ Also, some algorithms have additional capabilities in the `spark.ml` API; e.g.,
 provide class probabilities, and linear models provide model summaries.
 
 * [Feature extraction, transformation, and selection](ml-features.html)
-* [Clustering](ml-clustering.html)
 * [Decision Trees for classification and regression](ml-decision-tree.html)
 * [Ensembles](ml-ensembles.html)
 * [Linear methods with elastic net regularization](ml-linear-methods.html)
@@ -951,4 +950,4 @@ model.transform(test)
 {% endhighlight %}
 </div>
 
-</div>
\ No newline at end of file
+</div>
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index 54e35fcbb1..91e50ccfec 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -69,7 +69,6 @@ We list major functionality from both below, with links to detailed guides.
 concepts. It also contains sections on using algorithms within the Pipelines API, for example:
 
 * [Feature extraction, transformation, and selection](ml-features.html)
-* [Clustering](ml-clustering.html)
 * [Decision trees for classification and regression](ml-decision-tree.html)
 * [Ensembles](ml-ensembles.html)
 * [Linear methods with elastic net regularization](ml-linear-methods.html)
-- 
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