DSC_StreamKM: streamKM++

In streamMOA: Interface for MOA Stream Clustering Algorithms

streamKM++

Description

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.

Author(s)

Matthias Carnein

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()

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

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

streamMOA documentation built on Oct. 14, 2026, 1:06 a.m.