Github user chiwanpark commented on a diff in the pull request:
https://github.com/apache/flink/pull/1220#discussion_r61397260
--- Diff: docs/libs/ml/knn.md ---
@@ -0,0 +1,146 @@
+---
+mathjax: include
+htmlTitle: FlinkML - k-nearest neighbors
+title: <a href="../ml">FlinkML</a> - knn
+---
+<!--
+Licensed to the Apache Software Foundation (ASF) under one
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+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
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+KIND, either express or implied. See the License for the
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+-->
+
+* This will be replaced by the TOC
+{:toc}
+
+## Description
+Implements an exact k-nearest neighbors algorithm. Given a training set
$A$ and a testing set $B$, the algorithm returns
+
+$$
+KNN(A,B, k) = \{ \left( b, KNN(b,A) \right) where b \in B and KNN(b, A, k)
are the k-nearest points to b in A \}
+$$
+
+The brute-force approach is to compute the distance between every training
and testing point. To ease the brute-force computation of computing the
distance between every traning point a quadtree is used. The quadtree scales
well in the number of training points, though poorly in the spatial dimension.
The algorithm will automatically choose whether or not to use the quadtree,
though the user can override that decision by setting a parameter to force use
or not use a quadtree.
+
+##Operations
+
+`KNN` is a `Predictor`.
+As such, it supports the `fit` and `predict` operation.
+
+### Fit
+
+KNN is trained given a set of `LabeledVector`:
+
+* `fit: DataSet[LabeledVector] => Unit`
+
+### Predict
+
+KNN predicts for all subtypes of FlinkML's `Vector` the corresponding
class label:
+
+* `predict[T <: Vector]: DataSet[T] => DataSet[(T, Array[Vector])]`, where
the `(T, Array[Vector])` tuple
+ corresponds to (testPoint, K-nearest training points)
+
+## Paremeters
+The KNN implementation can be controlled by the following parameters:
+
+ <table class="table table-bordered">
+ <thead>
+ <tr>
+ <th class="text-left" style="width: 20%">Parameters</th>
+ <th class="text-center">Description</th>
+ </tr>
+ </thead>
+
+ <tbody>
+ <tr>
+ <td><strong>K</strong></td>
+ <td>
+ <p>
+ Defines the number of nearest-neoghbors to search for. That
is, for each test point, the algorithm finds the K nearest neighbors in the
training set
+ (Default value: <strong>5</strong>)
+ </p>
+ </td>
+ </tr>
+ <tr>
+ <td><strong> DistanceMetric</strong></td>
--- End diff --
Please remove space before DistanceMetric
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