path: root/docs/ml-clustering.md

---
layout: global
title: Clustering - spark.ml
displayTitle: Clustering - spark.ml
---

In this section, we introduce the pipeline API for [clustering in mllib](mllib-clustering.html).

**Table of Contents**

* This will become a table of contents (this text will be scraped).
{:toc}

## K-means

[k-means](http://en.wikipedia.org/wiki/K-means_clustering) is one of the
most commonly used clustering algorithms that clusters the data points into a
predefined number of clusters. The MLlib implementation includes a parallelized
variant of the [k-means++](http://en.wikipedia.org/wiki/K-means%2B%2B) method
called [kmeans||](http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf).

`KMeans` is implemented as an `Estimator` and generates a `KMeansModel` as the base model.

### Input Columns

<table class="table">
  <thead>
    <tr>
      <th align="left">Param name</th>
      <th align="left">Type(s)</th>
      <th align="left">Default</th>
      <th align="left">Description</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>featuresCol</td>
      <td>Vector</td>
      <td>"features"</td>
      <td>Feature vector</td>
    </tr>
  </tbody>
</table>

### Output Columns

<table class="table">
  <thead>
    <tr>
      <th align="left">Param name</th>
      <th align="left">Type(s)</th>
      <th align="left">Default</th>
      <th align="left">Description</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>predictionCol</td>
      <td>Int</td>
      <td>"prediction"</td>
      <td>Predicted cluster center</td>
    </tr>
  </tbody>
</table>

### Example

<div class="codetabs">

<div data-lang="scala" markdown="1">
Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.clustering.KMeans) for more details.

{% include_example scala/org/apache/spark/examples/ml/KMeansExample.scala %}
</div>

<div data-lang="java" markdown="1">
Refer to the [Java API docs](api/java/org/apache/spark/ml/clustering/KMeans.html) for more details.

{% include_example java/org/apache/spark/examples/ml/JavaKMeansExample.java %}
</div>

</div>


## 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">

<div data-lang="scala" markdown="1">

Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.clustering.LDA) for more details.

{% 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>