stream (version 0.2-0)

DSC_ClusTree: ClusTree Data Stream Clusterer

Description

Class implements the ClusTree cluster algorithm for data streams.

Usage

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).

Value

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

Details

This is an interface to the MOA implementation of ClusTree.

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

# 3 clusters with 5% noise
dsd <- DSD_GaussianStatic(k=3, noise=0)

dsc <- DSC_ClusTree(maxHeight=3)
cluster(dsc, dsd, 500)
dsc

# plot micro-clusters
plot(dsc, dsd)

# reclustering
kmeans <- DSC_Kmeans(k=3)
recluster(kmeans,dsc)
plot(kmeans, dsd)