Package edu.cmu.tetrad.cluster

Class KMeans

java.lang.Object

edu.cmu.tetrad.cluster.KMeans

All Implemented Interfaces:

ClusteringAlgorithm

public class KMeans
extends Object
implements ClusteringAlgorithm

Implements the "batch" version of the K Means clustering algorithm-- that is, in one sweep, assign each point to its nearest center, and then in a second sweep, reset each center to the mean of the cluster for that center, repeating until convergence.

Note that this algorithm is guaranteed to converge, since the total squared error is guaranteed to be reduced at each step.

Version:

$Id: $Id

Author:

josephramsey

Method Summary

Modifier and Type	Method	Description
void	cluster(Matrix data)	Clusters the given data set.
List<Integer>	getCluster(int k)	getCluster.
List<List<Integer>>	getClusters()	Getter for the field clusters.
int	getMaxIterations()	Return the maximum number of iterations, or -1 if the algorithm is allowed to run unconstrainted.
int	getNumClusters()	getNumClusters.
boolean	isVerbose()	isVerbose.
int	iterations()	iterations.
static KMeans	randomClusters(int numCenters)	Constructs a new KMeansBatch, initializing the algorithm by randomly assigning each point in the data to one of the numCenters clusters, then calculating the centroid of each cluster.
static KMeans	randomPoints(int numCenters)	Constructs a new KMeansBatch, initializing the algorithm by picking numCeneters centers randomly from the data itself.
void	setMaxIterations(int maxIterations)	Sets the maximum number of iterations, or -1 if the algorithm is allowed to run unconstrainted.
void	setVerbose(boolean verbose)	True iff verbose output should be printed.
String	toString()	toString.

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Method Details

randomPoints

public static KMeans randomPoints(int numCenters)

Constructs a new KMeansBatch, initializing the algorithm by picking numCeneters centers randomly from the data itself.

Parameters:

numCenters - The number of centers (clusters).

Returns:

The parametrized algorithm.

randomClusters

public static KMeans randomClusters(int numCenters)

Constructs a new KMeansBatch, initializing the algorithm by randomly assigning each point in the data to one of the numCenters clusters, then calculating the centroid of each cluster.

Parameters:

numCenters - The number of centers (clusters).

Returns:

The constructed algorithm.

cluster

public void cluster(Matrix data)

Clusters the given data set.

Runs the batch K-means clustering algorithm on the data, returning a result.

Specified by:

cluster in interface ClusteringAlgorithm

Parameters:

data - An n x m double matrix with n cases (rows) and m variables (columns). Makes an int array c such that c[i] is the cluster that case i is placed into, or -1 if case i is not placed into a cluster (as a result of its being eliminated from consideration, for instance).

getClusters

public List<List<Integer>> getClusters()

Getter for the field clusters.

Specified by:

getClusters in interface ClusteringAlgorithm

Returns:

a List object

getMaxIterations

public int getMaxIterations()

Return the maximum number of iterations, or -1 if the algorithm is allowed to run unconstrainted.

Returns:

This value.

setMaxIterations

public void setMaxIterations(int maxIterations)

Sets the maximum number of iterations, or -1 if the algorithm is allowed to run unconstrainted.

Parameters:

maxIterations - This value.

getNumClusters

public int getNumClusters()

getNumClusters.

Returns:

a int

getCluster

public List<Integer> getCluster(int k)

getCluster.

Parameters:

k - a int

Returns:

a List object

iterations

public int iterations()

iterations.

Returns:

the number of iterations.

toString

public String toString()

toString.

Overrides:

toString in class Object

Returns:

a string representation of the cluster result.

isVerbose

public boolean isVerbose()

isVerbose.

Returns:

a boolean

setVerbose

public void setVerbose(boolean verbose)

True iff verbose output should be printed.

Specified by:

setVerbose in interface ClusteringAlgorithm

Parameters:

verbose - True iff verbose output should be printed.