SOM | R Documentation |
Build a self-organizing map
SOM(
data,
xdim = 10,
ydim = 10,
rlen = 10,
mst = 1,
alpha = c(0.05, 0.01),
radius = stats::quantile(nhbrdist, 0.67) * c(1, 0),
init = FALSE,
initf = Initialize_KWSP,
distf = 2,
silent = FALSE,
map = TRUE,
codes = NULL,
importance = NULL
)
data |
Matrix containing the training data |
xdim |
Width of the grid |
ydim |
Hight of the grid |
rlen |
Number of times to loop over the training data for each MST |
mst |
Number of times to build an MST |
alpha |
Start and end learning rate |
radius |
Start and end radius |
init |
Initialize cluster centers in a non-random way |
initf |
Use the given initialization function if init == T (default: Initialize_KWSP) |
distf |
Distance function (1 = manhattan, 2 = euclidean, 3 = chebyshev, 4 = cosine) |
silent |
If FALSE, print status updates |
map |
If FALSE, data is not mapped to the SOM. Default TRUE. |
codes |
Cluster centers to start with |
importance |
array with numeric values. Parameters will be scaled according to importance |
A list containing all parameter settings and results
This code is strongly based on the kohonen
package.
R. Wehrens and L.M.C. Buydens, Self- and Super-organising Maps
in R: the kohonen package J. Stat. Softw., 21(5), 2007
BuildSOM
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