MOA 12.03
Real Time Analytics for Data Streams
moa.clusterers.KMeans Class Reference

A kMeans implementation for microclusterings. More...

List of all members.

Static Public Member Functions

static Clustering kMeans (Cluster[] centers, List<?extends Cluster > data)
 This kMeans implementation clusters a big number of microclusters into a smaller amount of macro clusters.
static Clustering gaussianMeans (Clustering gtClustering, Clustering clustering)

Detailed Description

A kMeans implementation for microclusterings.

For now it only uses the real centers of the groundtruthclustering for implementation. There should also be an option to use random centers. TODO: random centers TODO: Create a macro clustering interface to make different macro clustering algorithms available to micro clustering algorithms like clustream, denstream and clustree

Definition at line 39 of file KMeans.java.


Member Function Documentation

static Clustering moa.clusterers.KMeans.gaussianMeans ( Clustering  gtClustering,
Clustering  clustering 
) [static]

Definition at line 137 of file KMeans.java.

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static Clustering moa.clusterers.KMeans.kMeans ( Cluster[]  centers,
List<?extends Cluster data 
) [static]

This kMeans implementation clusters a big number of microclusters into a smaller amount of macro clusters.

To make it comparable to other algorithms it uses the real centers of the ground truth macro clustering to have the best possible initialization. The quality of resulting macro clustering yields an upper bound for kMeans on the underlying microclustering.

Parameters:
centersof the ground truth clustering
datalist of microclusters
Returns:

Definition at line 53 of file KMeans.java.

Referenced by moa.clusterers.KMeans.gaussianMeans().

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