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java.lang.Objectorg.encog.ml.kmeans.KMeansClustering
public class KMeansClustering
This class performs a basic K-Means clustering. This class can be used on either supervised or unsupervised data. For supervised data, the ideal values will be ignored. http://en.wikipedia.org/wiki/Kmeans
| Constructor Summary | |
|---|---|
KMeansClustering(int k,
MLDataSet theSet)
Construct the K-Means object. |
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| Method Summary | |
|---|---|
static double |
calculateEuclideanDistance(Centroid c,
MLData data)
Calculate the euclidean distance between a centroid and data. |
MLCluster[] |
getClusters()
|
double |
getWCSS()
|
void |
iteration()
Perform a single training iteration. |
void |
iteration(int count)
The number of iterations to perform. |
int |
numClusters()
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| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public KMeansClustering(int k,
MLDataSet theSet)
k - The number of clusters to use.theSet - The dataset to cluster.| Method Detail |
|---|
public static double calculateEuclideanDistance(Centroid c,
MLData data)
c - The centroid to use.data - The data to use.
public final MLCluster[] getClusters()
getClusters in interface MLClusteringpublic final double getWCSS()
public final void iteration()
iteration in interface MLClusteringpublic final void iteration(int count)
iteration in interface MLClusteringcount - The count of iterations.public final int numClusters()
numClusters in interface MLClustering
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