Clustering high dimensional data with Hidden Markov Model on Variable Blocks
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.
For a quick introduction to HDclust see the vignette vignette("HDclust").
Jia Li, Lin Lin and Yevhen Tupikov.
Maintainer: Yevhen Tupikov yzt116@psu.edu
Lin Lin and Jia Li, "Clustering with hidden Markov model on variable blocks," Journal of Machine Learning Research, 18(110):1-49, 2017.
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.