plot.deepMOU: Plotting method for "shallow" and deep mixtures of Unigrams...

In deepMOU: Clustering of Short Texts by Mixture of Unigrams and Its Deep Extensions

Description

Bi-dimensional representation (via Multi-Dimensional Scaling) of the clusters, where each bubble corresponds to a cluster, its size is proportional to the relative frequency of documents and color saturation reflects cluster cohesion.

Usage

## S3 method for class 'deepMOU'
plot(
  x,
  y,
  bubble_size = 1,
  bubble_col = c("red", "white"),
  cex_text = 1,
  main = NULL,
  ...
)

Arguments

x	Output from mou_EM, dir_mult_GD or deep_mou_gibbs.
y	Parameter not used
bubble_size	Graphical parameter for bubbles size.
bubble_col	Choose palette with two colors (default "red" and "white"). The first (darker) color will denote homogeneous clusters, while the latter (lighter) more heterogeneous ones.
cex_text	Size of texts inside bubbles.
main	A main title for the plot.
...	Parameter not used

Details

The default graphical representation of mou_EM, dir_mult_GD and deep_mou_gibbs is the bubble plot. Namely, a bi-dimensional representation (via Multi-Dimensional Scaling) of the clusters, each bubble corresponds to a cluster, its size is proportional to the relative frequency of documents and color saturation reflects cluster cohesion.

Value

A graphical aid to visualize and to describe the obtained clusters.

Examples

# Load the CNAE2 dataset
data("CNAE2")

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

# Usage of the function
plot(mou_CNAE2, bubble_size = 5 )

deepMOU documentation built on March 4, 2021, 9:09 a.m.