MOA 12.03
Real Time Analytics for Data Streams
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A kMeans implementation for microclusterings. More...
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) |
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.
static Clustering moa.clusterers.KMeans.gaussianMeans | ( | Clustering | gtClustering, |
Clustering | clustering | ||
) | [static] |
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.
centers | of the ground truth clustering |
data | list of microclusters |
Definition at line 53 of file KMeans.java.
Referenced by moa.clusterers.KMeans.gaussianMeans().