clustControl

This function creates a list with parameters for Modal Baum-Welch (MBW)
clustering algorithm used as an argument for <code>hmmvbClust</code>.

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) <https://jmlr.org/papers/v18/16-342.html>.

Jia Li

HDclust

Clustering High Dimensional Data with Hidden Markov Model on
Variable Blocks

Yevhen Tupikov

Lin Lin

Lixiang Zhang

clustControl function

<dl><dt>minSize</dt>
<dd>Minimum cluster size. Clusters that contain the number of data points
smaller than <code>minSize</code> are merged to the closest big cluster.</dd>
<dt>modeTh</dt>
<dd>Distance parameter that controls mode merging. Larger values 
promote merging of different clusters.</dd>
<dt>useL1norm</dt>
<dd>A logical value indicating whether or not L1 norm will be 
used to calculate the distance.</dd>
<dt>getlikelh</dt>
<dd>A logical value indicating whether or not to calculate the
loglikelihood for every data point.</dd></dl>

Arguments

Parameters for MBM clustering algorithm. — clustControl

<dl>

<dt>minSize</dt>
<dd>Minimum cluster size. Clusters that contain the number of data points
smaller than <code>minSize</code> are merged to the closest big cluster.</dd>


<dt>modeTh</dt>
<dd>Distance parameter that controls mode merging. Larger values 
promote merging of different clusters.</dd>


<dt>useL1norm</dt>
<dd>A logical value indicating whether or not L1 norm will be 
used to calculate the distance.</dd>


<dt>getlikelh</dt>
<dd>A logical value indicating whether or not to calculate the
loglikelihood for every data point.</dd>

</dl>

clustControl: Parameters for MBM clustering algorithm.

Description

Usage

Value

Arguments

See Also

Examples