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HDclust (version 1.0.4)

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

172

Version

1.0.4

License

GPL (>= 2)

Maintainer

Jia Li

Last Published

September 20th, 2024

Functions in HDclust (1.0.4)

Accessor for 'BIC' slot.

Parameters for MBM clustering algorithm.

Accessor for 'bdim' slot

Synthetic dataset used in section 5.1.2 of the reference paper.

Accessor for 'Loglikehd' slot.

Accessor for 'nb' slot

Accessor for 'clustParam' slot.

vbSearchControl

Parameters for variable block structure search.

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

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

Accessor for 'numst' slot

Hierarchical clustering of density modes

Accessor for 'HmmChain' slot.

Accessor for parameters of HMM

Accessor for 'optHMMVB' slot.

Cluster data with HMM-VB

Find density modes with HMM-VB

Make an instance of class "VB"

Parameters for HMM-VB training.

Accessor for 'dim' slot

Make an instance of "HMMVB" class.

Accessor for 'prenumst' slot

Accessor for 'size' slot.

Synthetic dataset used in section 5.1.3 of the reference paper

Accessor for 'VbStructure' slot.

Accessor for 'varorder' slot

Accessor for 'diagCov' slot.

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

HDclust-package

Clustering high dimensional data with Hidden Markov Model on Variable Blocks

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.

Make an instance of "HMM" class.