2015-11-30 17:56:51 -05:00
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layout: global
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2016-07-15 16:38:23 -04:00
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title: Clustering
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displayTitle: Clustering
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2015-11-30 17:56:51 -05:00
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---
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2016-07-15 16:38:23 -04:00
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This page describes clustering algorithms in MLlib.
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The [guide for clustering in the RDD-based API](mllib-clustering.html) also has relevant information
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about these algorithms.
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2015-11-30 17:56:51 -05:00
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2015-12-08 21:40:21 -05:00
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**Table of Contents**
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* This will become a table of contents (this text will be scraped).
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{:toc}
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2015-12-16 13:43:45 -05:00
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## K-means
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[k-means](http://en.wikipedia.org/wiki/K-means_clustering) is one of the
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most commonly used clustering algorithms that clusters the data points into a
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predefined number of clusters. The MLlib implementation includes a parallelized
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variant of the [k-means++](http://en.wikipedia.org/wiki/K-means%2B%2B) method
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called [kmeans||](http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf).
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`KMeans` is implemented as an `Estimator` and generates a `KMeansModel` as the base model.
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### Input Columns
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<table class="table">
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<thead>
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<tr>
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<th align="left">Param name</th>
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<th align="left">Type(s)</th>
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<th align="left">Default</th>
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<th align="left">Description</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>featuresCol</td>
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<td>Vector</td>
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<td>"features"</td>
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<td>Feature vector</td>
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</tr>
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</tbody>
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</table>
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### Output Columns
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<table class="table">
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<thead>
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<tr>
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<th align="left">Param name</th>
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<th align="left">Type(s)</th>
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<th align="left">Default</th>
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<th align="left">Description</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>predictionCol</td>
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<td>Int</td>
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<td>"prediction"</td>
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<td>Predicted cluster center</td>
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</tr>
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</tbody>
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</table>
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2016-11-08 09:04:07 -05:00
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**Examples**
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2015-12-16 13:43:45 -05:00
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.clustering.KMeans) for more details.
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{% include_example scala/org/apache/spark/examples/ml/KMeansExample.scala %}
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</div>
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<div data-lang="java" markdown="1">
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Refer to the [Java API docs](api/java/org/apache/spark/ml/clustering/KMeans.html) for more details.
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{% include_example java/org/apache/spark/examples/ml/JavaKMeansExample.java %}
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</div>
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2016-05-11 04:01:43 -04:00
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<div data-lang="python" markdown="1">
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Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.clustering.KMeans) for more details.
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{% include_example python/ml/kmeans_example.py %}
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</div>
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2016-12-05 03:39:44 -05:00
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<div data-lang="r" markdown="1">
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Refer to the [R API docs](api/R/spark.kmeans.html) for more details.
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2016-12-08 09:19:38 -05:00
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{% include_example r/ml/kmeans.R %}
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</div>
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2015-12-16 13:43:45 -05:00
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</div>
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2015-11-30 17:56:51 -05:00
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## Latent Dirichlet allocation (LDA)
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`LDA` is implemented as an `Estimator` that supports both `EMLDAOptimizer` and `OnlineLDAOptimizer`,
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2016-05-20 02:29:37 -04:00
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and generates a `LDAModel` as the base model. Expert users may cast a `LDAModel` generated by
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`EMLDAOptimizer` to a `DistributedLDAModel` if needed.
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2016-11-08 09:04:07 -05:00
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**Examples**
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2015-11-30 17:56:51 -05:00
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.clustering.LDA) for more details.
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{% include_example scala/org/apache/spark/examples/ml/LDAExample.scala %}
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</div>
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<div data-lang="java" markdown="1">
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Refer to the [Java API docs](api/java/org/apache/spark/ml/clustering/LDA.html) for more details.
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{% include_example java/org/apache/spark/examples/ml/JavaLDAExample.java %}
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</div>
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2016-05-11 06:49:41 -04:00
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<div data-lang="python" markdown="1">
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Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.clustering.LDA) for more details.
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2016-05-11 03:56:36 -04:00
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2016-05-11 06:49:41 -04:00
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{% include_example python/ml/lda_example.py %}
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</div>
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2016-12-08 09:19:38 -05:00
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<div data-lang="r" markdown="1">
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Refer to the [R API docs](api/R/spark.lda.html) for more details.
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{% include_example r/ml/lda.R %}
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</div>
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2016-05-11 06:49:41 -04:00
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</div>
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2016-05-11 03:56:36 -04:00
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2016-05-16 02:22:16 -04:00
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## Bisecting k-means
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2016-05-11 03:56:36 -04:00
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Bisecting k-means is a kind of [hierarchical clustering](https://en.wikipedia.org/wiki/Hierarchical_clustering) using a
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divisive (or "top-down") approach: all observations start in one cluster, and splits are performed recursively as one
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moves down the hierarchy.
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Bisecting K-means can often be much faster than regular K-means, but it will generally produce a different clustering.
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`BisectingKMeans` is implemented as an `Estimator` and generates a `BisectingKMeansModel` as the base model.
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2016-11-08 09:04:07 -05:00
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**Examples**
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2016-05-11 03:56:36 -04:00
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.clustering.BisectingKMeans) for more details.
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{% include_example scala/org/apache/spark/examples/ml/BisectingKMeansExample.scala %}
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</div>
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<div data-lang="java" markdown="1">
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Refer to the [Java API docs](api/java/org/apache/spark/ml/clustering/BisectingKMeans.html) for more details.
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{% include_example java/org/apache/spark/examples/ml/JavaBisectingKMeansExample.java %}
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</div>
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<div data-lang="python" markdown="1">
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Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.clustering.BisectingKMeans) for more details.
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{% include_example python/ml/bisecting_k_means_example.py %}
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</div>
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2017-02-03 15:19:47 -05:00
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<div data-lang="r" markdown="1">
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Refer to the [R API docs](api/R/spark.bisectingKmeans.html) for more details.
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{% include_example r/ml/bisectingKmeans.R %}
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</div>
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2016-05-11 03:56:36 -04:00
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</div>
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2016-05-17 09:20:47 -04:00
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## Gaussian Mixture Model (GMM)
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A [Gaussian Mixture Model](http://en.wikipedia.org/wiki/Mixture_model#Multivariate_Gaussian_mixture_model)
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represents a composite distribution whereby points are drawn from one of *k* Gaussian sub-distributions,
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each with its own probability. The `spark.ml` implementation uses the
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[expectation-maximization](http://en.wikipedia.org/wiki/Expectation%E2%80%93maximization_algorithm)
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algorithm to induce the maximum-likelihood model given a set of samples.
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`GaussianMixture` is implemented as an `Estimator` and generates a `GaussianMixtureModel` as the base
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model.
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### Input Columns
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<table class="table">
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<thead>
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<tr>
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<th align="left">Param name</th>
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<th align="left">Type(s)</th>
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<th align="left">Default</th>
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<th align="left">Description</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>featuresCol</td>
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<td>Vector</td>
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<td>"features"</td>
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<td>Feature vector</td>
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</tr>
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</tbody>
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</table>
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### Output Columns
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<table class="table">
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<thead>
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<tr>
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<th align="left">Param name</th>
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<th align="left">Type(s)</th>
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<th align="left">Default</th>
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<th align="left">Description</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td>predictionCol</td>
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<td>Int</td>
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<td>"prediction"</td>
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<td>Predicted cluster center</td>
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</tr>
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<tr>
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<td>probabilityCol</td>
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<td>Vector</td>
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<td>"probability"</td>
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<td>Probability of each cluster</td>
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</tr>
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</tbody>
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</table>
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2016-11-08 09:04:07 -05:00
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**Examples**
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2016-05-17 09:20:47 -04:00
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.clustering.GaussianMixture) for more details.
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{% include_example scala/org/apache/spark/examples/ml/GaussianMixtureExample.scala %}
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</div>
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<div data-lang="java" markdown="1">
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Refer to the [Java API docs](api/java/org/apache/spark/ml/clustering/GaussianMixture.html) for more details.
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{% include_example java/org/apache/spark/examples/ml/JavaGaussianMixtureExample.java %}
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</div>
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<div data-lang="python" markdown="1">
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Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.clustering.GaussianMixture) for more details.
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{% include_example python/ml/gaussian_mixture_example.py %}
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</div>
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2016-12-08 09:19:38 -05:00
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<div data-lang="r" markdown="1">
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Refer to the [R API docs](api/R/spark.gaussianMixture.html) for more details.
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{% include_example r/ml/gaussianMixture.R %}
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</div>
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2016-05-17 09:20:47 -04:00
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</div>
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