streamMOA (version 1.3-0)

DSC_ClusTree: ClusTree Data Stream Clusterer

Description

Interface for the MOA implementation of the ClusTree data stream clustering algorithm (Kranen et al, 2009).

Usage

DSC_ClusTree(horizon = 1000, maxHeight = 8, lambda = NULL, k = NULL)

Value

An object of class DSC_ClusTree (subclass of DSC, DSC_MOA, DSC_Micro).

Arguments

horizon

Range of the (time) window.

maxHeight

The maximum height of the tree.

lambda

number used to override computed lambda (decay).

k

If specified, k-means with k clusters is used for reclustering.

Author

Michael Hahsler and John Forrest

Details

ClusTree uses a compact and self-adaptive index structure for maintaining stream summaries. Kranen et al (2009) suggest EM or k-means for reclustering.

References

Philipp Kranen, Ira Assent, Corinna Baldauf, and Thomas Seidl. 2009. Self-Adaptive Anytime Stream Clustering. In Proceedings of the 2009 Ninth IEEE International Conference on Data Mining (ICDM '09). IEEE Computer Society, Washington, DC, USA, 249-258. tools:::Rd_expr_doi("10.1109/ICDM.2009.47")

Bifet A, Holmes G, Pfahringer B, Kranen P, Kremer H, Jansen T, Seidl T (2010). MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering. In Journal of Machine Learning Research (JMLR).

See Also

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

Examples

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

clustree <- DSC_ClusTree(maxHeight = 3)
update(clustree, stream, 500)
clustree

plot(clustree, stream)

#' Use automatically the k-means reclusterer with k = 3 to create macro clusters
clustree <- DSC_ClusTree(maxHeight = 3, k = 3)
update(clustree, stream, 500)
clustree

plot(clustree, stream, type = "both")