clusterCrit (version 1.3.0)

bestCriterion: Best clustering index

Description

bestCriterion returns the best index value according to a specified criterion.

Usage

bestCriterion(x, crit)

Value

The index in vector x of the best value according to the criterion specified by the crit argument.

Arguments

x

[matrix] : a numeric vector of quality index values.

crit

[character] : a string specifying the name of the criterion which was used to compute the quality indices.

Author

Bernard Desgraupes

University of Paris Ouest - Nanterre
Lab Modal'X (EA 3454)

Details

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.

See Also

getCriteriaNames, intCriteria.

Examples

# 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")