ml-clustering.md

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This page describes clustering algorithms in MLlib. The guide for clustering in the RDD-based API also has relevant information about these algorithms.

Table of Contents

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

K-means

k-means 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++ method called kmeans||.

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

Input Columns

Param name	Type(s)	Default	Description
featuresCol	Vector	"features"	Feature vector

Output Columns

Param name	Type(s)	Default	Description
predictionCol	Int	"prediction"	Predicted cluster center

Examples

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

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

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 %}

Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.clustering.KMeans) for more details.

{% include_example python/ml/kmeans_example.py %}

Refer to the R API docs for more details.

{% include_example r/ml/kmeans.R %}

Latent Dirichlet allocation (LDA)

LDA is implemented as an Estimator that supports both EMLDAOptimizer and OnlineLDAOptimizer, and generates a LDAModel as the base model. Expert users may cast a LDAModel generated by EMLDAOptimizer to a DistributedLDAModel if needed.

Examples

Refer to the Scala API docs for more details.

{% include_example scala/org/apache/spark/examples/ml/LDAExample.scala %}

Refer to the Java API docs for more details.

{% include_example java/org/apache/spark/examples/ml/JavaLDAExample.java %}

Refer to the Python API docs for more details.

{% include_example python/ml/lda_example.py %}

Refer to the R API docs for more details.

{% include_example r/ml/lda.R %}

Bisecting k-means

Bisecting k-means is a kind of hierarchical clustering using a divisive (or "top-down") approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.

Bisecting K-means can often be much faster than regular K-means, but it will generally produce a different clustering.

BisectingKMeans is implemented as an Estimator and generates a BisectingKMeansModel as the base model.

Examples

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

{% include_example scala/org/apache/spark/examples/ml/BisectingKMeansExample.scala %}

Refer to the [Java API docs](api/java/org/apache/spark/ml/clustering/BisectingKMeans.html) for more details.

{% include_example java/org/apache/spark/examples/ml/JavaBisectingKMeansExample.java %}

Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.clustering.BisectingKMeans) for more details.

{% include_example python/ml/bisecting_k_means_example.py %}

Refer to the R API docs for more details.

{% include_example r/ml/bisectingKmeans.R %}

Gaussian Mixture Model (GMM)

A Gaussian Mixture Model represents a composite distribution whereby points are drawn from one of k Gaussian sub-distributions, each with its own probability. The spark.ml implementation uses the expectation-maximization algorithm to induce the maximum-likelihood model given a set of samples.

GaussianMixture is implemented as an Estimator and generates a GaussianMixtureModel as the base model.

Input Columns

Param name	Type(s)	Default	Description
featuresCol	Vector	"features"	Feature vector

Output Columns

Param name	Type(s)	Default	Description
predictionCol	Int	"prediction"	Predicted cluster center
probabilityCol	Vector	"probability"	Probability of each cluster

Examples

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

{% include_example scala/org/apache/spark/examples/ml/GaussianMixtureExample.scala %}

Refer to the [Java API docs](api/java/org/apache/spark/ml/clustering/GaussianMixture.html) for more details.

{% include_example java/org/apache/spark/examples/ml/JavaGaussianMixtureExample.java %}

Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.clustering.GaussianMixture) for more details.

{% include_example python/ml/gaussian_mixture_example.py %}

Refer to the R API docs for more details.

{% include_example r/ml/gaussianMixture.R %}

Power Iteration Clustering (PIC)

Power Iteration Clustering (PIC) is a scalable graph clustering algorithm developed by Lin and Cohen. From the abstract: PIC finds a very low-dimensional embedding of a dataset using truncated power iteration on a normalized pair-wise similarity matrix of the data.

spark.ml's PowerIterationClustering implementation takes the following parameters:

• k: the number of clusters to create
• initMode: param for the initialization algorithm
• maxIter: param for maximum number of iterations
• srcCol: param for the name of the input column for source vertex IDs
• dstCol: name of the input column for destination vertex IDs
• weightCol: Param for weight column name

Examples

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

{% include_example scala/org/apache/spark/examples/ml/PowerIterationClusteringExample.scala %}

Refer to the [Java API docs](api/java/org/apache/spark/ml/clustering/PowerIterationClustering.html) for more details.

{% include_example java/org/apache/spark/examples/ml/JavaPowerIterationClusteringExample.java %}

Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.clustering.PowerIterationClustering) for more details.

{% include_example python/ml/power_iteration_clustering_example.py %}

Refer to the R API docs for more details.

{% include_example r/ml/powerIterationClustering.R %}