the topology used to build the grid.The following are permitted:
"hexagonal""rectangular"
rlen
the maximum number of iterations to be done
alpha
learning rate, a vector of two numbers indicating the
amount of change. Default is to decline linearly from 0.05 to 0.01
over rlen updates.
radius
the radius of the neighbourhood, either given as a
single number or a vector (start, stop). If it is given as a single
number the radius will run from the given number to the negative
value of that number; as soon as the neighbourhood gets smaller than
one only the winning unit will be updated.
index
vector of the index to be calculated. This should be one of : "db","dunn",
"silhouette","ptbiserial","ch","cindex","ratkowsky","mcclain","gamma",
"gplus","tau","ccc","scott","marriot","trcovw","tracew","friedman",
"rubin","ball","sdbw","dindex","hubert","sv","xie-beni","hartigan",
"ssi","xu","rayturi","pbm","banfeld","all"(all indices will be used)
Value
All.index.by.layer
Values of indices for each layer.
Best.nc
Best number of clusters proposed by each index and the corresponding index value.
Best.partition
Partition that corresponds to the best number of clusters
Author(s)
Sarra Chair and Malika Charrad
Examples
## A 3-dimensional example
set.seed(1)
x <- rbind(matrix(rnorm(150,sd=0.3),ncol=3),
matrix(rnorm(150,mean=1,sd=0.3),ncol=3),
matrix(rnorm(150,mean=2,sd=0.3),ncol=3))
res<-multisom.stochastic(x, xheight = 7, xwidth = 7,"hexagonal",
rlen = 100,alpha = c(0.05, 0.01),
radius = c(2, 1.3, 1.2, 1),"sv")
res$All.index.by.layer
res$Best.nc
## A real data example
data<-iris[,-c(5)]
res<-multisom.stochastic(data, xheight = 8, xwidth = 8,"hexagonal",
rlen = 100,alpha = c(0.05, 0.01),
radius = c(2, 1.5, 1.2, 1),c("db","ratkowsky","dunn"))
res$All.index.by.layer
res$Best.nc