Package moa.clusterers
Class KMeans
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- moa.clusterers.KMeans
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public class KMeans extends Object
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
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Constructor Summary
Constructors Constructor Description KMeans()
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Method Summary
All Methods Static Methods Concrete Methods Modifier and Type Method Description static Clustering
gaussianMeans(Clustering gtClustering, Clustering clustering)
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.
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Method Detail
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kMeans
public 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. 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:
centers
- of the ground truth clusteringdata
- list of microclusters- Returns:
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gaussianMeans
public static Clustering gaussianMeans(Clustering gtClustering, Clustering clustering)
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