#' wheel data set with inlier and outlier.
#'
#' A bivariate dataset with an inlier and anoutlier
#'
#' @format A data frame with 1002 rows and 3 variables:
#' \describe{
#' \item{x }{numerical variable}
#' \item{y }{numerical variable}
#' \item{type}{Type of a data point : Typical or Outlier}}
"wheel1"
#' A dataset with an outlier
#'
#' A bivariate dataset with an outlier
#'
#' @format A data frame with 1001 rows and 3 variables:
#' \describe{
#' \item{x }{numerical variable}
#' \item{y }{numerical variable}
#' \item{type}{Type of a data point : Typical or Outlier}}
"data_a"
#' A bimodal dataset with a micro cluster
#'
#' A bivariate dataset with two typical classes and a micor cluster
#'
#' @format A data frame with 2003 rows and 3 variables:
#' \describe{
#' \item{x }{numerical variable}
#' \item{y }{numerical variable}
#' \item{type}{Type of a data point : Typical or Outlier}}
"data_b"
#' A dataset with local anomalies and micro clusters
#'
#' A bivariate dataset with local anomalies and two micro clusters
#'
#' @format A data frame with 1009 rows and 3 variables:
#' \describe{
#' \item{x }{numerical variable}
#' \item{y }{numerical variable}
#' \item{type}{Type of a data point : Typical or Outlier}}
"data_c"
#' A wheel dataset with two inliers
#'
#' A bivariate dataset with two inliers. The inliers are very close to one another
#'
#' @format A data frame with 1002 rows and 3 variables:
#' \describe{
#' \item{x }{numerical variable}
#' \item{y }{numerical variable}
#' \item{type}{Type of a data point : Typical or Outlier}}
"data_d"
#' A bimodal dataset with an inlier
#'
#' A bimodal dataset with an inlier. one typical class is a very dense cluster,
#'
#' @format A data frame with 2001 rows and 3 variables:
#' \describe{
#' \item{x }{numerical variable}
#' \item{y }{numerical variable}
#' \item{type}{Type of a data point : Typical or Outlier}}
"data_e"
#' A dataset with an outlier
#'
#' A dataset with an outlier. The typical class is a very dense cluster.
#'
#' @format A data frame with 2001 rows and 3 variables:
#' \describe{
#' \item{x }{numerical variable}
#' \item{y }{numerical variable}
#' \item{type}{Type of a data point : Typical or Outlier}}
"data_f"
#' Dataset with pedestrian counts
#'
#' A dataset with hourly pedestrian counts at 43 locations in the
#' city Melbourne, australia, from 1 December, 2018 to 1, January, 2019.
#'
#' @format A data frame with 33024 rows and 5 variables:
#' \describe{
#' \item{Sensor }{Sensor location}
#' \item{Date_Time }{Time and date }
#' \item{Date}{Date}
#' \item{Time}{Time}
#' \item{Count}{Pedestrian count}}
"ped_data"
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