% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{temperature_city.int}
\alias{temperature_city.int}
\title{World Cities Monthly Temperature Interval Dataset}
\format{
A data frame with 6 observations and 13 columns (6 monthly interval
variables in \code{_l}/\code{_u} Min-Max pairs, plus a label). Only
January through June are included:
\itemize{
    \item \code{city}: City name (character).
    \item \code{jan_l}, \code{jan_u}: January temperature range (degrees Celsius).
    \item \code{feb_l}, \code{feb_u}: February temperature range.
    \item \code{mar_l}, \code{mar_u}: March temperature range.
    \item \code{apr_l}, \code{apr_u}: April temperature range.
    \item \code{may_l}, \code{may_u}: May temperature range.
    \item \code{jun_l}, \code{jun_u}: June temperature range.
}
}
\usage{
data(temperature_city.int)
}
\description{
Interval-valued monthly temperatures for major cities worldwide.
Benchmark dataset for comparing distance measures (Hausdorff, L2,
Wasserstein) in dynamic clustering algorithms.
}
\details{
Expert partition into 4 classes: Class 1 (tropical/warm), Class 2
(temperate European and Asian), Class 3 (Mauritius), Class 4 (Tehran).
}
\section{Metadata}{

\tabular{ll}{
  \strong{Sample size (n)} \tab 6 \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(temperature_city.int)
}
\references{
Verde, R. and Irpino, A. (2008). A new interval data distance based on
the Wasserstein metric. \emph{Proc. COMPSTAT 2008}, pp. 705-712.
}
\keyword{clustering}
\keyword{datasets}
\keyword{distance}
\keyword{interval}
