library(handwriter)
# view cluster fill counts for template training documents
template_data <- format_template_data(example_cluster_template)
plot_cluster_fill_counts(template_data, facet = TRUE)
Example cluster template
Description
An example cluster template created with make_clustering_template(). The cluster template was created from handwriting samples "w0016_s01_pLND_r01.png", "w0080_s01_pLND_r01.png", "w0124_s01_pLND_r01.png", "w0138_s01_pLND_r01.png", and "w0299_s01_pLND_r01.png" from the CSAFE Handwriting Database. The template has K=5 clusters.
Usage
example_cluster_template
Format
A list containing a single cluster template created by make_clustering_template(). 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:
- centers_seed
- An integer for the random number generator.
- cluster
- A vector of cluster assignments for each graph used to create the cluster template.
- centers
- The final cluster centers produced by 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.