ClusTree Data Stream Clusterer

Interface to 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)

Arguments

horizon

Length of the time window.

maxHeight

Maximum height of the tree.

lambda

Value used to override the computed decay parameter.

k

If specified, use k-means with k clusters for reclustering.

Value

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

Details

ClusTree uses a compact, self-adaptive index structure to maintain 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. 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()

Author

Michael Hahsler and John Forrest

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
#> ClusTree 
#> Class: moa/clusterers/clustree/ClusTree, DSC_MOA, DSC_Micro, DSC 
#> Number of micro-clusters: 27 

plot(clustree, stream)


# Use the k-means reclusterer with k = 3 to create macro-clusters
clustree <- DSC_ClusTree(maxHeight = 3, k = 3)
update(clustree, stream, 500)
clustree
#> ClusTree + k-Means (weighted) 
#> Class: DSC_TwoStage, DSC_Macro, DSC 
#> Number of micro-clusters: 29 
#> Number of macro-clusters: 3 

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