mushroom

<p>Categorical data from UCI Machine Learning Repository describing 8124
mushrooms with 22 phenotype variables. Each mushroom is classified as "edible"
or "poisonous" and the goal is to recover the mushroom class from its
phenotype.</p>

datasets

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

mushroom function

<p>An R data.frame with a variable edibility used as label and 22
 categorical variables with no names. More detail on the UCI webpage
 describing the data.</p>

mushroom: Mushroom data

Description

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Examples