Different hierarchical clusterings and k-means clusterings as well as a model-based clustering have been applied to several financial variables for a random sample of ten thousand observations.
data(CPScluster)A data frame with 10000 observations on the following 39 variables.
Agea numeric vector
Sexa factor with levels female male
Racea factor with levels Black White
Ethnica factor
Marital.Statusa factor
Kind.of.Familya factor
Classicala factor with levels All other Classical Husband-Wife family
Family.Typea factor
Number.of.Persons.in.Familya numeric vector
Number.of.Kidsa numeric vector
Education.of.Heada factor
Labor.Statusa factor
Class.of.Workera factor
Working.Hoursa numeric vector
Income.of.Heada numeric vector
Family.Incomea numeric vector
Taxable.Incomea numeric vector
Federal.taxa numeric vector
Family.sequence.numbera numeric vector
Statea factor
Divisiona factor
Regiona factor with levels Midwest North East South West
hc4a numeric vector
hc6a numeric vector
hc8a numeric vector
hc12a numeric vector
hcs4a numeric vector
hcs6a numeric vector
hcs8a numeric vector
hcs12a numeric vector
hcw4a numeric vector
hcw6a numeric vector
hcw8a numeric vector
hcw12a numeric vector
km4a numeric vector
km6a numeric vector
km8a numeric vector
km12a numeric vector
mc12a numeric vector
# NOT RUN {
data(CPScluster)
## maybe str(CPScluster) ; plot(CPScluster) ...
# }
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