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
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.
Examples
{% include_example scala/org/apache/spark/examples/ml/KMeansExample.scala %}
{% include_example java/org/apache/spark/examples/ml/JavaKMeansExample.java %}
{% include_example python/ml/kmeans_example.py %}
Refer to the R API docs for more details.
{% include_example r/ml/kmeans.R %}
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 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
{% include_example scala/org/apache/spark/examples/ml/BisectingKMeansExample.scala %}
{% include_example java/org/apache/spark/examples/ml/JavaBisectingKMeansExample.java %}
{% include_example python/ml/bisecting_k_means_example.py %}
Refer to the R API docs for more details.
{% include_example r/ml/bisectingKmeans.R %}
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.
Examples
{% include_example scala/org/apache/spark/examples/ml/GaussianMixtureExample.scala %}
{% include_example java/org/apache/spark/examples/ml/JavaGaussianMixtureExample.java %}
{% include_example python/ml/gaussian_mixture_example.py %}
Refer to the R API docs for more details.
{% include_example r/ml/gaussianMixture.R %}