deepMOU (version 0.1.1)

mou_EM: Mixture of Unigrams by Expectation-Maximization algorithm

Description

Performs parameter estimation by means of the Expectation-Maximization (EM) algorithm and cluster allocation for the Mixture of Unigrams.

Usage

mou_EM(x, k, n_it = 100, eps = 1e-07, seed_choice = 1, init = NULL)

Arguments

x

Document-term matrix describing the frequency of terms that occur in a collection of documents. Rows correspond to documents in the collection and columns correspond to terms.

k

Number of clusters/groups.

n_it

Number of iterations for the Expectation-Maximization algorithm.

eps

Tolerance level for the convergence of the algorithm. Default is 1e-07.

seed_choice

Set seed for reproducible results

init

Vector containing the initial document allocations for the initialization of the algorithm. If NULL (default) initialization is carried out via spherical k-means (skmeans).

Value

A list containing the following elements:

x

The data matrix.

clusters

the clustering labels.

k

the number of clusters.

numobs

the sample size.

p

the vocabulary size.

likelihood

vector containing the likelihood values at each iteration.

pi_hat

estimated probabilities of belonging to the k clusters.

omega

matrix containing the estimates of the omega parameters for each cluster.

f_z_x

matrix containing the posterior probabilities of belonging to each cluster.

AIC

Akaike Information Criterion (AIC) value of the fitted model.

BIC

Bayesian Information Criterion (BIC) value of the fitted model.

Details

Starting from the data given by x the Mixture of Unigrams is fitted and k clusters are obtained. The algorithm for the parameter estimation is the Expectation-Maximization (EM). In particular, the function assigns initial values to weights of the Multinomial distribution for each cluster and inital weights for the components of the mixture. The estimates are obtained with maximum n_it steps of the EM algorithm or until the tolerance level eps is reached; by using the posterior distribution of the latent variable z, the documents are allocated to the cluster which maximizes the posterior distribution. For further details see the references.

References

Nigam, K., McCallum, A., Thrun, S., Mitchell, T.: Text classification from labeled and unlabeled documents using EM. Machine learning 39, 103-134 (2000).

Examples

# NOT RUN {
# Load the CNAE2 dataset
data("CNAE2")

# Perform parameter estimation and clustering
mou_CNAE2 = mou_EM(x = CNAE2, k = 2)

# Shows cluster labels to documents
mou_CNAE2$clusters

# }