KMeansClustering

Documentation of 'org.neuroph.nnet.learning.kmeans.KMeansClustering' Java class KMeansClustering

```
public class KMeansClustering
extends java.lang.Object
```
1. Pick an initial set of K centroids (this can be random or any other means) 2. For each data point, assign it to the member of the closest centroid according to the given distance function 3. Adjust the centroid position as the mean of all its assigned member data points. Go back to (2) until the membership isn't change and centroid position is stable.

Constructors
Constructor and Description
`KMeansClustering(DataSet dataSet)`
`KMeansClustering(DataSet dataSet, int numberOfClusters)`

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`doClustering()`
`Cluster[]`	`getClusters()`
`DataSet`	`getDataSet()`
`java.lang.String`	`getLog()`
`void`	`initClusters()`
`void`	`setDataSet(DataSet vectors)`
`void`	`setNumberOfClusters(int numberOfClusters)`

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

public KMeansClustering(DataSet dataSet)

public KMeansClustering(DataSet dataSet,
                        int numberOfClusters)

public void setDataSet(DataSet vectors)

public void setNumberOfClusters(int numberOfClusters)

Class KMeansClustering