by JimmyR
Last Updated August 10, 2018 12:19 PM

I am building an anomaly model and am confused between these distances below. What is the difference between these distances in self organized maps.

som.iris$distances

dist(som.iris$codes[[1]])

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.

thanks.

```
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$codes[[1]]))$order,20)
```

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