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
4.3 KiB
layout | title |
---|---|
global | <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
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 %}
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 %}