clprofiles: Profiling k-Prototypes Clustering

In clustMixType: k-Prototypes Clustering for Mixed Variable-Type Data

Profiling k-Prototypes Clustering

Description

Visualization of a k-prototypes clustering result for cluster interpretation.

Usage

clprofiles(object, x, vars = NULL, col = NULL)

Arguments

object	Object resulting from a call of resulting kproto. Also other kmeans like objects with object$cluster and object$size are possible.
x	Original data.
vars	Optional vector of either column indices or variable names.
col	Palette of cluster colours to be used for the plots. As a default RColorBrewer's brewer.pal(max(unique(object$cluster)), "Set3") is used for k > 2 clusters and lightblue and orange else.

Details

For numerical variables boxplots and for factor variables barplots of each cluster are generated.

Author(s)

[e-mail]

Examples

# generate toy data with factors and numerics

n   <- 100
prb <- 0.9
muk <- 1.5 
clusid <- rep(1:4, each = n)

x1 <- sample(c("A","B"), 2*n, replace = TRUE, prob = c(prb, 1-prb))
x1 <- c(x1, sample(c("A","B"), 2*n, replace = TRUE, prob = c(1-prb, prb)))
x1 <- as.factor(x1)

x2 <- sample(c("A","B"), 2*n, replace = TRUE, prob = c(prb, 1-prb))
x2 <- c(x2, sample(c("A","B"), 2*n, replace = TRUE, prob = c(1-prb, prb)))
x2 <- as.factor(x2)

x3 <- c(rnorm(n, mean = -muk), rnorm(n, mean = muk), rnorm(n, mean = -muk), rnorm(n, mean = muk))
x4 <- c(rnorm(n, mean = -muk), rnorm(n, mean = muk), rnorm(n, mean = -muk), rnorm(n, mean = muk))

x <- data.frame(x1,x2,x3,x4)

# apply k-prototyps
kpres <- kproto(x, 4)
clprofiles(kpres, x)

# in real world clusters are often not as clear cut
# by variation of lambda the emphasize is shifted towards factor / numeric variables    
kpres <- kproto(x, 2)
clprofiles(kpres, x)

kpres <- kproto(x, 2, lambda = 0.1)
clprofiles(kpres, x)

kpres <- kproto(x, 2, lambda = 25)
clprofiles(kpres, x)

clustMixType documentation built on July 1, 2024, 5:08 p.m.