jml.clustering
Class KMeans

java.lang.Object
  extended by jml.clustering.Clustering
      extended by jml.clustering.KMeans

public class KMeans
extends Clustering

A Java implementation for KMeans.

Version:
1.0, Jan. 3rd, 2013
Author:
Mingjie Qian

Field Summary
(package private)  KMeansOptions options
           
 
Fields inherited from class jml.clustering.Clustering
centers, dataMatrix, indicatorMatrix, nClus, nFeature, nSample
 
Constructor Summary
KMeans(int nClus)
           
KMeans(int nClus, int maxIter)
           
KMeans(int nClus, int maxIter, boolean verbose)
           
KMeans(KMeansOptions options)
           
 
Method Summary
 void clustering()
          Initializer needs not be explicitly specified.
static void main(java.lang.String[] args)
           
static void runKMeans()
           
 
Methods inherited from class jml.clustering.Clustering
clustering, feedData, feedData, getAccuracy, getCenters, getData, getIndicatorMatrix, initialize
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Field Detail

options

KMeansOptions options
Constructor Detail

KMeans

public KMeans(int nClus)

KMeans

public KMeans(int nClus,
              int maxIter)

KMeans

public KMeans(int nClus,
              int maxIter,
              boolean verbose)

KMeans

public KMeans(KMeansOptions options)
Method Detail

clustering

public void clustering()
Initializer needs not be explicitly specified. If the initial indicator matrix is not given, random initialization will be used.

Specified by:
clustering in class Clustering

main

public static void main(java.lang.String[] args)
Parameters:
args -

runKMeans

public static void runKMeans()