Best clustering index
bestCriterion returns the best index value according to a specified criterion.
bestCriterion(x, crit)
x |
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crit |
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Given a vector of several clustering quality index values computed
with a given criterion, the function bestCriterion returns the
index of the "best" one in the sense of the specified criterion.
Typically, a set of data has been clusterized several times (using
different algorithms or specifying a different number of clusters) and
a clustering index has been calculated each time : the
bestCriterion function tells which value is considered the best
according to the given clustering index. For instance, if one uses
the Calinski_Harabasz index, the best value is the largest one.
A list of all the supported criteria can be obtained with the
getCriteriaNames function. The criterion name
(crit argument) is case insensitive and can be abbreviated.
The index in vector x of the best value according to the criterion
specified by the crit argument.
Bernard Desgraupes
bernard.desgraupes@u-paris10.fr
University of Paris Ouest - Nanterre
Lab Modal'X (EA 3454)
# Create some spheric data around three distinct centers
x <- rbind(matrix(rnorm(100, mean = 0, sd = 0.5), ncol = 2),
matrix(rnorm(100, mean = 2, sd = 0.5), ncol = 2),
matrix(rnorm(100, mean = 4, sd = 0.5), ncol = 2))
vals <- vector()
for (k in 2:6) {
# Perform the kmeans algorithm
cl <- kmeans(x, k)
# Compute the Calinski_Harabasz index
vals <- c(vals,as.numeric(intCriteria(x,cl$cluster,"Calinski_Harabasz")))
}
idx <- bestCriterion(vals,"Calinski_Harabasz")
cat("Best index value is",vals[idx],"\n")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.