% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{china_temp_monthly.int}
\alias{china_temp_monthly.int}
\title{China Monthly Temperature Intervals (15 Stations)}
\format{
A symbolic data frame (\code{symbolic_tbl}) with 15 observations
(weather stations) and 13 variables:
\itemize{
    \item \code{January}, \code{February}, \code{March}, \code{April},
          \code{May}, \code{June}, \code{July}, \code{August},
          \code{September}, \code{October}, \code{November}, \code{December}:
          Interval-valued monthly temperature ranges (degrees Celsius).
    \item \code{Elevation}: Station elevation above sea level (numeric, meters).
}
Row names are station names (e.g., BoKeTu, Hailaer, LaSa).
}
\source{
Billard, L. and Diday, E. (2020), Table 7-9.
}
\usage{
data(china_temp_monthly.int)
}
\description{
Interval-valued dataset of monthly temperature ranges for 15 weather
stations in China. Each station has 12 monthly temperature intervals
(minimum and maximum observed temperatures in degrees Celsius) and
an elevation value in meters.
}
\section{Metadata}{

\tabular{ll}{
  \strong{Sample size (n)} \tab 15 \cr
  \strong{Variables (p)} \tab 13 \cr
  \strong{Subject area} \tab Climate \cr
  \strong{Symbolic format} \tab Interval \cr
  \strong{Analytical tasks} \tab Clustering
}
}

\examples{
data(china_temp_monthly.int)
}
\references{
Billard, L. and Diday, E. (2020). \emph{Clustering Methodology for
Symbolic Data}. Wiley, Chichester. Table 7-9.
}
\keyword{climate}
\keyword{datasets}
\keyword{interval}
\keyword{temperature}
