% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{census.mix}
\alias{census.mix}
\title{Census Mixed Symbolic Dataset}
\format{
A symbolic data frame (\code{symbolic_tbl}) with 10 observations
(regions) and 6 variables:
\itemize{
    \item \code{age}: Histogram-valued age distribution (12 age bins).
    \item \code{home_value}: Histogram-valued home value distribution
          (7 value bins, in $1000s).
    \item \code{gender}: Distribution over gender (male, female).
    \item \code{fuel}: Multi-valued set of fuel types used.
    \item \code{tenure}: Distribution over housing tenure
          (owner, renter, vacant).
    \item \code{income}: Interval-valued household income range ($1000s).
}
Row names are Region_1 through Region_10.
}
\source{
Billard, L. and Diday, E. (2020), Table 7-23.
}
\usage{
data(census.mix)
}
\description{
Mixed symbolic dataset of 10 census regions combining 6 different symbolic
variable types: histograms (age, home value), distributions (gender,
tenure), a multi-valued set (fuel), and an interval (income).
}
\section{Metadata}{

\tabular{ll}{
  \strong{Sample size (n)} \tab 10 \cr
  \strong{Variables (p)} \tab 6 \cr
  \strong{Subject area} \tab Demographics \cr
  \strong{Symbolic format} \tab Mixed (interval, histogram, distribution, multi-valued) \cr
  \strong{Analytical tasks} \tab Clustering
}
}

\examples{
data(census.mix)
}
\references{
Billard, L. and Diday, E. (2020). \emph{Clustering Methodology for
Symbolic Data}. Wiley, Chichester. Table 7-23.
}
\keyword{datasets}
\keyword{distribution}
\keyword{histogram}
\keyword{interval}
\keyword{mixed}
