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HDclust (version 1.0)

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. Lin Lin and Jia Li (2017) .

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Version

Install

install.packages('HDclust')

Monthly Downloads

226

Version

1.0

License

GPL (>= 2)

Maintainer

Yevhen Tupikov

Last Published

July 29th, 2018

Functions in HDclust (1.0)

Class "HMMVB" to represent a Hidden Markov Model on Variable Blocks (HMM-VB).

Accessor for 'bdim' slot

Class "HMMVBBIC" to represent results of HMM-VB model selection.

HMMVBclust-class

Class "HMMVBclust" to represent clustering results with Hidden Markov Model on variable block structure.

Accessor for 'clsid' slot.

Class "VB" to represent a variable block structure.

HDclust-package

Clustering high dimensional data with Hidden Markov Model on Variable Blocks

Class "HMM" to represent parameters associated with a variable block in the HMM-VB

Accessor for 'optHMMVB' slot.

Accessor for 'diagCov' slot.

Parameters for MBM clustering algorithm.

Accessor for 'HmmChain' slot.

Accessor for 'BIC' slot.

Cluster data with HMM-VB

Accessor for parameters of HMM

Synthetic dataset used in section 5.1.2 of the reference paper.

Accessor for 'dim' slot

Accessor for 'nb' slot

Accessor for 'numst' slot

Accessor for 'prenumst' slot

Accessor for 'configList' slot.

Accessor for 'clustParam' slot.

Synthetic dataset used in section 5.1.3 of the reference paper

Accessor for 'VbStructure' slot.

Accessor for 'size' slot.

vbSearchControl

Parameters for variable block structure search.

Accessor for 'varorder' slot

Make an instance of "HMM" class.

Make an instance of "HMMVB" class.

Parameters for HMM-VB training.

Make an instance of class "VB"