I am building an anomaly model and am confused between these distances below. What is the difference between these distances in self organized maps.
As far as I have read, 1) is between vector to Best Matching Unit, and 2) is the euclidean distance between adjacent neurons in the som topology (also seen in U-matrix) . When do i use 1. and when do i use 2.? How are they associated with each other? is 1. a more granular version of 2.? I'm interested to assign a score based on how 'different' one is against another.
library('kohonen') set.seed(1) train <- iris # --------- unsupervised Training - Train Model ------------ #preprocess train.sc <- scale(train[,-5]) #train model som_grid <- somgrid(xdim = 5 ,ydim=5 ,topo="hexagonal" ,toroidal = F) som.iris<- som(train.sc ,grid=som_grid ,rlen=200 ,alpha=c(0.05,0.01) ,keep.data = TRUE ) #different distances #cells in descending order based on distance between vector and BMU head(som.iris$unit.classif[order(som.iris$distances,decreasing=T)],20) #cells in descending order between adjacent neurons in som map head(hclust(dist(som.iris$codes[]))$order,20)