| DSC_ClusTree {streamMOA} | R Documentation |
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)
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. Kranen et al (2009) suggest EM or k-means for reclustering.
Value
An object of class DSC_ClusTree (subclass of stream::DSC,
DSC_MOA, stream::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
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")