Package cc.mallet.cluster

Class KMeans

java.lang.Object

cc.mallet.cluster.Clusterer

cc.mallet.cluster.KMeans

All Implemented Interfaces:

java.io.Serializable

public class KMeans
extends Clusterer

KMeans Clusterer Clusters the points into k clusters by minimizing the total intra-cluster variance. It uses a given Metric to find the distance between Instances, which should have SparseVectors in the data field.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field	Description
static int	EMPTY_DROP	Drop an empty cluster
static int	EMPTY_ERROR	Treat an empty cluster as an error condition.
static int	EMPTY_SINGLE	Place the single instance furthest from the previous cluster mean

Constructor Summary

Constructors

Constructor	Description
KMeans(Pipe instancePipe, int numClusters, Metric metric)	Construct a KMeans object
KMeans(Pipe instancePipe, int numClusters, Metric metric, int emptyAction)	Construct a KMeans object

Method Summary

Modifier and Type	Method	Description
Clustering	cluster(InstanceList instances)	Cluster instances
java.util.ArrayList<SparseVector>	getClusterMeans()	Return the ArrayList of cluster means after a run of the algorithm.

Methods inherited from class cc.mallet.cluster.Clusterer

getPipe

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

EMPTY_ERROR

public static final int EMPTY_ERROR

Treat an empty cluster as an error condition.

See Also:

Constant Field Values

EMPTY_DROP

public static final int EMPTY_DROP

Drop an empty cluster

See Also:

Constant Field Values

EMPTY_SINGLE

public static final int EMPTY_SINGLE

Place the single instance furthest from the previous cluster mean

See Also:

Constant Field Values

Constructor Detail

KMeans

public KMeans(Pipe instancePipe,
              int numClusters,
              Metric metric,
              int emptyAction)

Construct a KMeans object

Parameters:

instancePipe - Pipe for the instances being clustered

numClusters - Number of clusters to use

metric - Metric object to measure instance distances

emptyAction - Specify what should happen when an empty cluster occurs

KMeans

public KMeans(Pipe instancePipe,
              int numClusters,
              Metric metric)

Construct a KMeans object

Parameters:

instancePipe - Pipe for the instances being clustered

numClusters - Number of clusters to use

metric - Metric object to measure instance distances

If an empty cluster occurs, it is considered an error.

Method Detail

cluster

public Clustering cluster(InstanceList instances)

Cluster instances

Specified by:

cluster in class Clusterer

Parameters:

instances - List of instances to cluster

getClusterMeans

public java.util.ArrayList<SparseVector> getClusterMeans()

Return the ArrayList of cluster means after a run of the algorithm.

Returns:

An ArrayList of Instances.