assign.clusters

Assign sequences to cluster models that give the highest sequence-to-hmm
likelihood. Used in <code>hmm.clust</code>.

Provides an implementation of a mixture of hidden Markov models
(HMMs) for discrete sequence data in the Discrete Bayesian HMM Clustering
(DBHC) algorithm. The DBHC algorithm is an HMM Clustering
algorithm that finds a mixture of discrete-output HMMs while using
heuristics based on Bayesian Information Criterion (BIC) to search for the
optimal number of HMM states and the optimal number of clusters.

Gabriel Budel

DBHC

Sequence Clustering with Discrete-Output HMMs

assign.clusters function

<dl><dt>partition</dt>
<dd>A list object with the partition, a mixture of HMMs. Each
element in the list is an <code>hmm</code> object (see
<code>build_hmm</code>).</dd>
<dt>memberships</dt>
<dd>A matrix with cluster memberships for each sequence.</dd>
<dt>sequences</dt>
<dd>An <code>stslist</code> object (see
<code>seqdef</code>) of sequences with discrete observations.</dd>
<dt>smoothing</dt>
<dd>Smoothing parameter for absolute discounting in
<code>smooth.probabilities</code>.</dd></dl>

Arguments

Cluster Assignment — assign.clusters

<dl>

<dt>partition</dt>
<dd>A list object with the partition, a mixture of HMMs. Each
element in the list is an <code>hmm</code> object (see
<code>build_hmm</code>).</dd>


<dt>memberships</dt>
<dd>A matrix with cluster memberships for each sequence.</dd>


<dt>sequences</dt>
<dd>An <code>stslist</code> object (see
<code>seqdef</code>) of sequences with discrete observations.</dd>


<dt>smoothing</dt>
<dd>Smoothing parameter for absolute discounting in
<code>smooth.probabilities</code>.</dd>

</dl>

assign.clusters: Cluster Assignment

Description

Usage

Value

Arguments

See Also