dist.minmax {stylo}

Min-Max Distance (aka Ruzicka Distance)

Description

Function for computing a similarity measure bewteen two (or more) vectors. Some scholars (Kestemont et at., 2016) claim that it works well when applied to authorship attribution problems.

Usage

dist.minmax(x)

Arguments

x

a matrix or data table containing at least 2 rows and 2 cols, the samples (texts) to be compared in rows, the variables in columns.

Value

The function returns an object of the class dist, containing distances between each pair of samples. To convert it to a square matrix instead, use the generic function as.dist.

Author(s)

Maciej Eder

References

Kestemont, M., Stover, J., Koppel, M., Karsdorp, F. and Daelemans, W. (2016). Authenticating the writings of Julius Caesar. Expert Systems With Applications, 63: 86-96.

See Also

stylo, classify, dist, as.dist, dist.cosine

Examples

# first, preparing a table of word frequencies
        Iuvenalis_1 = c(3.939, 0.635, 1.143, 0.762, 0.423)
        Iuvenalis_2 = c(3.733, 0.822, 1.066, 0.933, 0.511)
        Tibullus_1  = c(2.835, 1.302, 0.804, 0.862, 0.881)
        Tibullus_2  = c(2.911, 0.436, 0.400, 0.946, 0.618)
        Tibullus_3  = c(1.893, 1.082, 0.991, 0.879, 1.487)
        dataset = rbind(Iuvenalis_1, Iuvenalis_2, Tibullus_1, Tibullus_2, 
                        Tibullus_3)
        colnames(dataset) = c("et", "non", "in", "est", "nec")

# the table of frequencies looks as follows
        print(dataset)
        
# then, applying a distance, in two flavors
        dist.minmax(dataset)
        as.matrix(dist.minmax(dataset))