kmeans_clustersRProg {SOMEnv}

K-means algorithm applied for different values of clusters

Description

The som_kmeansR function with 100 epochs training is run for a custom number of times for each k value of clusters and the best of these is selected based on sum of squared errors (err). The Davies-Bouldin index is calculated for each k-clustering. The function has been coded in R code starting from kmeans_clusters.m script present in somtoolbox for Matlab by Vesanto and adapted to show a progress bar when working embedded in the shiny app.

Usage

kmeans_clustersRProg(codebook, k = 5, times = 5, seed = NULL)

Arguments

codebook

SOM codebook

k

Maximum number of clusters (the function will be run from 2 to k clusters)

times

Number of times the som_kmeansR function is iterated

seed

Number for set.seed function

Value

This function returns a list containing the cluster number assignment for each sample, the cluster centroids, the total quantization error, the DB-index for each number of clusters, and the random seed number used

Author(s)

Sabina Licen, Pierluigi Barbieri

References

J. Vesanto, J. Himberg, E. Alhoniemi, J. Parhankagas, SOM Toolbox for Matlab 5, Report A57, 2000, Available at: www.cis.hut.fi/projects/somtoolbox/package/papers/techrep.pdf

See Also

som_mdistR, som_kmeansRProg, db_indexR