A cluster template created by 'handwriter' with K=40 clusters. This template was created from 100 handwriting samples from the CSAFE Handwriting Database. This template is suitable for casework.
templateK40A list containing the contents of the cluster template.
An integer for the random number generator use to select the starting cluster centers for the K-Means algorithm.
A vector of cluster assignments for each graph used to create the cluster template. The clusters are numbered sequentially 1, 2,...,K.
The final cluster centers produced by the K-Means algorithm.
The number of clusters in the template.
The number of training graphs to used to create the template.
A vector that lists the training document from which each graph originated.
A vector that lists the writer of each graph.
The maximum number of iterations for the K-means algorithm.
A vector of the number of graphs that changed clusters on each iteration of the K-means algorithm.
A vector of the outlier cutoff values calculated on each iteration of the K-means algorithm.
The reason the K-means algorithm terminated.
The within cluster distances on the final 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. The output of 'handwriter::make_clustering_template' stores the within cluster distances on each iteration, but the previous iterations were removed here to reduce the file size.
A vector of the within-cluster sum of squares on each iteration of the K-means algorithm.
'handwriter' splits handwriting samples into component shapes called graphs. The graphs are sorted into 40 clusters with a K-Means algorithm. See 'handwriter' for more details.
# view number of clusters
templateK40$K
# view number of iterations
templateK40$iters
# view cluster centers
templateK40$centers
Run the code above in your browser using DataLab