DSC_ClusTree: ClusTree Data Stream Clusterer

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

View source: R/DSC_ClusTree.R

Description

Class implements the ClusTree cluster algorithm for data streams.

Usage

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

Arguments

horizon

Range of the (time) window.

maxHeight

The maximum height of the tree.

lambda

number used to override computed lambda (decay).

Details

This is an interface to the MOA implementation of ClusTree.

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)

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

# plot micro-clusters
plot(clustree, stream)

# recluster with k-means
kmeans <- DSC_Kmeans(k=3)
recluster(kmeans, clustree)
plot(kmeans, 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, 500)
plot(CTxReach, stream, type="both")

streamMOA documentation built on April 7, 2018, 9:04 a.m.