Skip to content
Snippets Groups Projects
  • Xiangrui Meng's avatar
    26d35f3f
    [SPARK-1506][MLLIB] Documentation improvements for MLlib 1.0 · 26d35f3f
    Xiangrui Meng authored
    Preview: http://54.82.240.23:4000/mllib-guide.html
    
    Table of contents:
    
    * Basics
      * Data types
      * Summary statistics
    * Classification and regression
      * linear support vector machine (SVM)
      * logistic regression
      * linear linear squares, Lasso, and ridge regression
      * decision tree
      * naive Bayes
    * Collaborative Filtering
      * alternating least squares (ALS)
    * Clustering
      * k-means
    * Dimensionality reduction
      * singular value decomposition (SVD)
      * principal component analysis (PCA)
    * Optimization
      * stochastic gradient descent
      * limited-memory BFGS (L-BFGS)
    
    Author: Xiangrui Meng <meng@databricks.com>
    
    Closes #422 from mengxr/mllib-doc and squashes the following commits:
    
    944e3a9 [Xiangrui Meng] merge master
    f9fda28 [Xiangrui Meng] minor
    9474065 [Xiangrui Meng] add alpha to ALS examples
    928e630 [Xiangrui Meng] initialization_mode -> initializationMode
    5bbff49 [Xiangrui Meng] add imports to labeled point examples
    c17440d [Xiangrui Meng] fix python nb example
    28f40dc [Xiangrui Meng] remove localhost:4000
    369a4d3 [Xiangrui Meng] Merge branch 'master' into mllib-doc
    7dc95cc [Xiangrui Meng] update linear methods
    053ad8a [Xiangrui Meng] add links to go back to the main page
    abbbf7e [Xiangrui Meng] update ALS argument names
    648283e [Xiangrui Meng] level down statistics
    14e2287 [Xiangrui Meng] add sample libsvm data and use it in guide
    8cd2441 [Xiangrui Meng] minor updates
    186ab07 [Xiangrui Meng] update section names
    6568d65 [Xiangrui Meng] update toc, level up lr and svm
    162ee12 [Xiangrui Meng] rename section names
    5c1e1b1 [Xiangrui Meng] minor
    8aeaba1 [Xiangrui Meng] wrap long lines
    6ce6a6f [Xiangrui Meng] add summary statistics to toc
    5760045 [Xiangrui Meng] claim beta
    cc604bf [Xiangrui Meng] remove classification and regression
    92747b3 [Xiangrui Meng] make section titles consistent
    e605dd6 [Xiangrui Meng] add LIBSVM loader
    f639674 [Xiangrui Meng] add python section to migration guide
    c82ffb4 [Xiangrui Meng] clean optimization
    31660eb [Xiangrui Meng] update linear algebra and stat
    0a40837 [Xiangrui Meng] first pass over linear methods
    1fc8271 [Xiangrui Meng] update toc
    906ed0a [Xiangrui Meng] add a python example to naive bayes
    5f0a700 [Xiangrui Meng] update collaborative filtering
    656d416 [Xiangrui Meng] update mllib-clustering
    86e143a [Xiangrui Meng] remove data types section from main page
    8d1a128 [Xiangrui Meng] move part of linear algebra to data types and add Java/Python examples
    d1b5cbf [Xiangrui Meng] merge master
    72e4804 [Xiangrui Meng] one pass over tree guide
    64f8995 [Xiangrui Meng] move decision tree guide to a separate file
    9fca001 [Xiangrui Meng] add first version of linear algebra guide
    53c9552 [Xiangrui Meng] update dependencies
    f316ec2 [Xiangrui Meng] add migration guide
    f399f6c [Xiangrui Meng] move linear-algebra to dimensionality-reduction
    182460f [Xiangrui Meng] add guide for naive Bayes
    137fd1d [Xiangrui Meng] re-organize toc
    a61e434 [Xiangrui Meng] update mllib's toc
    26d35f3f
    History
    [SPARK-1506][MLLIB] Documentation improvements for MLlib 1.0
    Xiangrui Meng authored
    Preview: http://54.82.240.23:4000/mllib-guide.html
    
    Table of contents:
    
    * Basics
      * Data types
      * Summary statistics
    * Classification and regression
      * linear support vector machine (SVM)
      * logistic regression
      * linear linear squares, Lasso, and ridge regression
      * decision tree
      * naive Bayes
    * Collaborative Filtering
      * alternating least squares (ALS)
    * Clustering
      * k-means
    * Dimensionality reduction
      * singular value decomposition (SVD)
      * principal component analysis (PCA)
    * Optimization
      * stochastic gradient descent
      * limited-memory BFGS (L-BFGS)
    
    Author: Xiangrui Meng <meng@databricks.com>
    
    Closes #422 from mengxr/mllib-doc and squashes the following commits:
    
    944e3a9 [Xiangrui Meng] merge master
    f9fda28 [Xiangrui Meng] minor
    9474065 [Xiangrui Meng] add alpha to ALS examples
    928e630 [Xiangrui Meng] initialization_mode -> initializationMode
    5bbff49 [Xiangrui Meng] add imports to labeled point examples
    c17440d [Xiangrui Meng] fix python nb example
    28f40dc [Xiangrui Meng] remove localhost:4000
    369a4d3 [Xiangrui Meng] Merge branch 'master' into mllib-doc
    7dc95cc [Xiangrui Meng] update linear methods
    053ad8a [Xiangrui Meng] add links to go back to the main page
    abbbf7e [Xiangrui Meng] update ALS argument names
    648283e [Xiangrui Meng] level down statistics
    14e2287 [Xiangrui Meng] add sample libsvm data and use it in guide
    8cd2441 [Xiangrui Meng] minor updates
    186ab07 [Xiangrui Meng] update section names
    6568d65 [Xiangrui Meng] update toc, level up lr and svm
    162ee12 [Xiangrui Meng] rename section names
    5c1e1b1 [Xiangrui Meng] minor
    8aeaba1 [Xiangrui Meng] wrap long lines
    6ce6a6f [Xiangrui Meng] add summary statistics to toc
    5760045 [Xiangrui Meng] claim beta
    cc604bf [Xiangrui Meng] remove classification and regression
    92747b3 [Xiangrui Meng] make section titles consistent
    e605dd6 [Xiangrui Meng] add LIBSVM loader
    f639674 [Xiangrui Meng] add python section to migration guide
    c82ffb4 [Xiangrui Meng] clean optimization
    31660eb [Xiangrui Meng] update linear algebra and stat
    0a40837 [Xiangrui Meng] first pass over linear methods
    1fc8271 [Xiangrui Meng] update toc
    906ed0a [Xiangrui Meng] add a python example to naive bayes
    5f0a700 [Xiangrui Meng] update collaborative filtering
    656d416 [Xiangrui Meng] update mllib-clustering
    86e143a [Xiangrui Meng] remove data types section from main page
    8d1a128 [Xiangrui Meng] move part of linear algebra to data types and add Java/Python examples
    d1b5cbf [Xiangrui Meng] merge master
    72e4804 [Xiangrui Meng] one pass over tree guide
    64f8995 [Xiangrui Meng] move decision tree guide to a separate file
    9fca001 [Xiangrui Meng] add first version of linear algebra guide
    53c9552 [Xiangrui Meng] update dependencies
    f316ec2 [Xiangrui Meng] add migration guide
    f399f6c [Xiangrui Meng] move linear-algebra to dimensionality-reduction
    182460f [Xiangrui Meng] add guide for naive Bayes
    137fd1d [Xiangrui Meng] re-organize toc
    a61e434 [Xiangrui Meng] update mllib's toc
layout: global
title: <a href="mllib-guide.html">MLlib</a> - Clustering
  • Table of contents {:toc}

Clustering

Clustering is an unsupervised learning problem whereby we aim to group subsets of entities with one another based on some notion of similarity. Clustering is often used for exploratory analysis and/or as a component of a hierarchical supervised learning pipeline (in which distinct classifiers or regression models are trained for each cluster).

MLlib supports k-means clustering, one of the most commonly used clustering algorithms that clusters the data points into predfined number of clusters. The MLlib implementation includes a parallelized variant of the k-means++ method called kmeans||. The implementation in MLlib has the following parameters:

  • k is the number of desired clusters.
  • maxIterations is the maximum number of iterations to run.
  • initializationMode specifies either random initialization or initialization via k-means||.
  • runs is the number of times to run the k-means algorithm (k-means is not guaranteed to find a globally optimal solution, and when run multiple times on a given dataset, the algorithm returns the best clustering result).
  • initializiationSteps determines the number of steps in the k-means|| algorithm.
  • epsilon determines the distance threshold within which we consider k-means to have converged.

Examples

Following code snippets can be executed in `spark-shell`.

In the following example after loading and parsing data, we use the KMeans object to cluster the data into two clusters. The number of desired clusters is passed to the algorithm. We then compute Within Set Sum of Squared Error (WSSSE). You can reduce this error measure by increasing k. In fact the optimal k is usually one where there is an "elbow" in the WSSSE graph.

{% highlight scala %} import org.apache.spark.mllib.clustering.KMeans import org.apache.spark.mllib.linalg.Vectors

// Load and parse the data val data = sc.textFile("data/kmeans_data.txt") val parsedData = data.map(s => Vectors.dense(s.split(' ').map(_.toDouble)))

// Cluster the data into two classes using KMeans val numClusters = 2 val numIterations = 20 val clusters = KMeans.train(parsedData, numClusters, numIterations)

// Evaluate clustering by computing Within Set Sum of Squared Errors val WSSSE = clusters.computeCost(parsedData) println("Within Set Sum of Squared Errors = " + WSSSE) {% endhighlight %}

All of MLlib's methods use Java-friendly types, so you can import and call them there the same way you do in Scala. The only caveat is that the methods take Scala RDD objects, while the Spark Java API uses a separate `JavaRDD` class. You can convert a Java RDD to a Scala one by calling `.rdd()` on your `JavaRDD` object.
Following examples can be tested in the PySpark shell.

In the following example after loading and parsing data, we use the KMeans object to cluster the data into two clusters. The number of desired clusters is passed to the algorithm. We then compute Within Set Sum of Squared Error (WSSSE). You can reduce this error measure by increasing k. In fact the optimal k is usually one where there is an "elbow" in the WSSSE graph.

{% highlight python %} from pyspark.mllib.clustering import KMeans from numpy import array from math import sqrt

Load and parse the data

data = sc.textFile("data/kmeans_data.txt") parsedData = data.map(lambda line: array([float(x) for x in line.split(' ')]))

Build the model (cluster the data)

clusters = KMeans.train(parsedData, 2, maxIterations=10, runs=10, initializationMode="random")

Evaluate clustering by computing Within Set Sum of Squared Errors

def error(point): center = clusters.centers[clusters.predict(point)] return sqrt(sum([x**2 for x in (point - center)]))

WSSSE = parsedData.map(lambda point: error(point)).reduce(lambda x, y: x + y) print("Within Set Sum of Squared Error = " + str(WSSSE)) {% endhighlight %}