wkmeans: Weighted k-means for mixed-type data

Description

Weighted k-means for mixed continuous and categorical variables. A user-specified weight conWeight controls the relative contribution of the variable types to the cluster solution.

Usage

wkmeans(conData, catData, conWeight, nclust, ...)

Arguments

conData

The continuous variables. Must be coercible to a data frame.

catData

The categorical variables, either as factors or dummy-coded variables. Must be coercible to a data frame.

conWeight

The continuous weight; must be between 0 and 1. The categorical weight is 1-conWeight.

nclust

The number of clusters.

...

Optional arguments passed to kmeans.

Value

A stats::kmeans results object, with additional slots conCenters and catCenters giving the actual centers adjusted for the weighting process.

Details

A simple adaptation of stats::kmeans to mixed-type data. Continuous variables are multiplied by the input parameter conWeight, and categorical variables are multipled by 1-conWeight. If factor variables are input to catData, they are transformed to 0-1 dummy coded variables with the function dummyCodeFactorDf.

Examples

Run this code

# NOT RUN {
# Generate toy data set with poor quality categorical variables and good
# quality continuous variables.
set.seed(1)
dat <- genMixedData(200, nConVar=2, nCatVar=2, nCatLevels=4, nConWithErr=2,
  nCatWithErr=2, popProportions=c(.5,.5), conErrLev=0.3, catErrLev=0.8)
catDf <- data.frame(apply(dat$catVars, 2, factor), stringsAsFactors = TRUE)
conDf <- data.frame(scale(dat$conVars), stringsAsFactors = TRUE)

# A clustering that emphasizes the continuous variables
r1 <- with(dat,wkmeans(conDf, catDf, 0.9, 2))
table(r1$cluster, dat$trueID)

# A clustering that emphasizes the categorical variables; note argument
# passed to the underlying stats::kmeans function
r2 <- with(dat,wkmeans(conDf, catDf, 0.1, 2, nstart=4))
table(r2$cluster, dat$trueID)
# }