multisom.stochastic {multisom}

Multisom for stochastic version

Description

This function implements the stochastic version of MultiSOM algorithm.

Usage

multisom.stochastic(data = NULL, xheight = 7, xwidth = 7,
                  topo = c("rectangular", "hexagonal"),
                  neighbouhood.fct =c("bubble","gaussian"),
                  dist.fcts = NULL, rlen = 100,alpha = c(0.05, 0.01),
                  radius = c(2, 1.5, 1.2, 1), index = "all")

Arguments

data

the data matrix of observations

xheight

the x-dimension of the map

xwidth

the y-dimension of the map

topo

the topology used to build the grid.The following are permitted: "hexagonal" "rectangular"

neighbouhood.fct

the neighbouhood function type. The following are permitted: "gaussian" "bubble"

dist.fcts

The metric used to determine the distance function. Possible choices are: "sumofsquares" "euclidean" "manhattan" "tanimoto"

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 real data example

data<-as.matrix(iris[,-c(5)])

res<-multisom.stochastic(data, xheight = 8, xwidth = 8,"hexagonal","gaussian",
                    dist.fcts = NULL, 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