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streamKM++

Source: R/DSC_streamkm.R
DSC_StreamKM.Rd

This is an interface to the MOA implementation of streamKM++.

Usage

DSC_StreamKM(sizeCoreset = 10000, numClusters = 5, length = 100000L, ...)

Arguments

sizeCoreset

Size of the coreset.

numClusters

Number of clusters to compute.

length

Maximum number of points in the data stream.

...

Further arguments (currently ignored).

Details

streamKM++ uses a tree-based sampling strategy to build a small weighted sample of the stream (a coreset). The MOA implementation applies k-means++ to find the requested number of centers in the coreset.

Notes

  • The clusterer can process at most length points. Processing more points causes an ArrayIndexOutOfBoundsException.

  • The coreset is not exposed as micro-clusters; only macro-clusters can be requested.

References

Marcel R. Ackermann, Christiane Lammersen, Marcus Maertens, Christoph Raupach, Christian Sohler, Kamil Swierkot. StreamKM++: A Clustering Algorithm for Data Streams. In: Proceedings of the 12th Workshop on Algorithm Engineering and Experiments (ALENEX '10), 2010.

See also

Other DSC_MOA: DSC_BICO_MOA(), DSC_CluStream(), DSC_ClusTree(), DSC_DStream_MOA(), DSC_DenStream(), DSC_MCOD(), DSC_MOA()

Author

Matthias Carnein

Examples

set.seed(1000)
stream <- DSD_Gaussians(k = 3, d = 2, noise = 0.05)

# cluster with streamKM++
streamkm <- DSC_StreamKM(sizeCoreset = 100, numClusters = 3, length = 1000)
update(streamkm, stream, 100)
streamkm
#> StreamKM 
#> Class: moa/clusterers/streamkm/StreamKM, DSC_MOA, DSC_Micro, DSC 
#> Number of macro-clusters: 3 

# plot macro-clusters (no access to micro-clusters)
plot(streamkm, stream)