Cluster data with HMM-VB
This function clusters dataset with HMM-VB. First, for each data point it finds an optimal state sequence using Viterbi algorithm. Next, it uses Modal Baum-Welch algorithm (MBW) to find the modes of distinct Viterbi state sequences. Data points associated the same modes form clusters. If different data sets are clustered using the same HMM-VB, clustering results of one data set can be supplied as a reference during clustering of another data set to produce aligned clusters.
hmmvbClust(data, model = NULL, control = clustControl(), rfsClust = NULL, nthread = 1, bicObj = NULL)
data |
A numeric vector, matrix, or data frame of observations. Categorical values are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables. |
model |
An object of class 'HMMVB' that contains trained HMM-VB obtained
by the call to function |
control |
A list of control parameters for clustering. The defaults are set by
the call |
rfsClust |
A list of parameters for the reference cluster that can be used
for alignment. See |
nthread |
An integer specifying the number of threads used in clustering. |
bicObj |
An object of class 'HMMVBBIC' which stores results of model selection.
If provided, argument |
An object of class 'HMMVBclust'.
# cluster using trained HMM-VB Vb <- vb(1, dim=4, numst=2) set.seed(12345) hmmvb <- hmmvbTrain(iris[,1:4], VbStructure=Vb) clust <- hmmvbClust(iris[,1:4], model=hmmvb) show(clust) pairs(iris[,1:4], col=getClsid(clust)) # cluster using HMMVBBIC object obtained in model selection Vb <- vb(1, dim=4, numst=1) set.seed(12345) modelBIC <- hmmvbBIC(iris[,1:4], VbStructure=Vb) clust <- hmmvbClust(iris[,1:4], bicObj=modelBIC) show(clust) pairs(iris[,1:4], col=getClsid(clust))
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