Compute the normalized mutual information of two discrete samples

An ensemble of algorithms that enable the clustering of networks and data matrices (such as counts, categorical or continuous) with different type of generative models. Model selection and clustering is performed in combination by optimizing the Integrated Classification Likelihood (which is equivalent to minimizing the description length). Several models are available such as: Stochastic Block Model, degree corrected Stochastic Block Model, Mixtures of Multinomial, Latent Block Model. The optimization is performed thanks to a combination of greedy local search and a genetic algorithm (see <arXiv:2002:11577> for more details).

Etienne C<c3><b4>me

greed

Clustering and Model Selection with the Integrated
Classification Likelihood

Etienne Côme

Nicolas Jouvin

NMI function

<dl><dt>cl1</dt>
<dd>vector of discrete labels</dd>
<dt>cl2</dt>
<dd>vector of discrete labels</dd></dl>

Arguments

Compute the normalized mutual information of two discrete samples — NMI

<dl>

<dt>cl1</dt>
<dd>vector of discrete labels</dd>


<dt>cl2</dt>
<dd>vector of discrete labels</dd>

</dl>

NMI: Compute the normalized mutual information of two discrete samples

Description

Usage

Value

Arguments

Examples