HDclust-package: Clustering high dimensional data with Hidden Markov Model on...

In HDclust: Clustering High Dimensional Data with Hidden Markov Model on Variable Blocks

Clustering high dimensional data with Hidden Markov Model on Variable Blocks

Description

Clustering of high dimensional data with Hidden Markov Model on Variable Blocks (HMM-VB) fitted via Baum-Welch algorithm. Clustering is performed by the Modal Baum-Welch algorithm (MBW), which finds modes of the density function.

Details

For a quick introduction to HDclust see the vignette vignette("HDclust").

Author(s)

Lin Lin, Yevhen Tupikov, Lixiang Zhang and Jia Li.

Maintainer: Jia Li [e-mail]

References

Lin Lin and Jia Li, "Clustering with hidden Markov model on variable blocks," Journal of Machine Learning Research, 18(110):1-49, 2017.

See Also

hmmvbTrain, hmmvbClust

Examples

data("sim3")
set.seed(12345)
Vb <- vb(2, dim=40, bdim=c(10,30), numst=c(3,5), varorder=list(c(1:10),c(11:40)))
hmmvb <- hmmvbTrain(sim3[,1:40], VbStructure=Vb)
clust <- hmmvbClust(sim3[,1:40], model=hmmvb)
show(clust)

HDclust documentation built on Sept. 20, 2024, 5:09 p.m.