example_cluster_template: Example cluster template

In handwriter: Handwriting Analysis in R

Example cluster template

Description

An example cluster template created from the template training example handwriting documents included in the package. These documents are located in system.file("extdata/example_images/template_training_images", package = "handwriter"). The cluster template was created with K=10 clusters and a small, random sample of 1000 graphs.

Usage

example_cluster_template

Format

A list containing a single cluster template created by make_clustering_templates(). The cluster template was created by sorting a random sample of 1000 graphs from 10 training documents into 10 clusters with a K-means algorithm. The cluster template is a named list with 16 items:

seed

An integer for the random number generator.

cluster

A vector of cluster assignments for each graph used to create the cluster template.

centers

A list of graphs used as the starting cluster centers for the K-means algorithm.

K

The number of clusters to build (10) with the K-means algorithm.

n

The number of training graphs to use (1000) in the K-means algorithm.

docnames

A vector that lists the training document from which each graph originated.

writers

A vector that lists the writer of each graph.

iters

The maximum number of iterations for the K-means algorithm (3).

changes

A vector of the number of graphs that changed clusters on each iteration of the K-means algorithm.

outlierCutoff

A vector of the outlier cutoff values calculated on each iteration of the K-means algorithm.

stop_reason

The reason the K-means algorithm terminated.

wcd

A matrix of the within cluster distances on each iteration of the K-means algorithm. More specifically, the distance between each graph and the center of the cluster to which it was assigned on each iteration.

wcss

A vector of the within-cluster sum of squares on each iteration of the K-means algorithm.

rmse

A vector of the root-mean square error on each iteration of the K-means algorithm.

DaviesBouldinIndex

The Davies-Bouldin index on each iteration of the K-means algorithm.

VarianceRatioCriterion

The variance-ratio criterion on each iteration of the K-means algorithm.

Examples

# view cluster fill counts for template training documents
template_data <- format_template_data(example_cluster_template)
plot_cluster_fill_counts(template_data, facet = TRUE)

handwriter documentation built on Oct. 13, 2023, 5:10 p.m.