DSC_ClusTree: ClusTree Data Stream Clusterer

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

Interface for the MOA implementation of the ClusTree data stream clustering algorithm.

Usage

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

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.

Details

ClusTree uses a compact and self-adaptive index structure for maintaining stream summaries.

Value

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

Author(s)

Michael Hahsler and John Forrest

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. DOI=10.1109/ICDM.2009.47 http://dx.doi.org/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

DSC, DSC_Micro, DSC_MOA

Examples

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# data with 3 clusters and 5% noise
stream <- DSD_Gaussians(k=3, d=2, noise=0.05)

# 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 micro-clusters
plot(clustree, stream, , type = "both")

# create a two stage clusering using ClusTree and reachability reclustering
CTxReach <- DSC_TwoStage(
  micro=DSC_ClusTree(maxHeight=3),
  macro=DSC_Reachability(epsilon = .15)
)
CTxReach
update(CTxReach, stream, 1000)
plot(CTxReach, stream, type = "both")

streamMOA documentation built on May 16, 2019, 1:07 a.m.