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#' @title Cross-Entropy Clustering
#'
#' @description CEC divides data into Gaussian type clusters. The implementation
#' allows the simultaneous use of various type Gaussian mixture models,
#' performs the reduction of unnecessary clusters and it's able to discover new
#' groups. Based on Spurek, P. and Tabor, J. (2014) <doi:10.1016/j.patcog.2014.03.006>
#' \code{cec}.
#'
#' @name CEC-package
#'
#' @docType package
#'
#' @author Konrad Kamieniecki
#'
#' @seealso \code{\link{cec}}
#'
#' @keywords package multivariate cluster models
#'
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#' @title Four Gaussian Clusters
#'
#' @description Matrix of 2-dimensional points forming four Gaussian clusters.
#'
#' @name fourGaussians
#'
#' @docType data
#'
#' @keywords datasets
#'
#' @examples
#' data(fourGaussians)
#' plot(fourGaussians, cex = 0.5, pch = 19);
#'
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#' @title Three Gaussian Clusters
#'
#' @description Matrix of 2-dimensional points forming three Gaussian clusters.
#'
#' @name threeGaussians
#'
#' @docType data
#'
#' @keywords datasets
#'
#' @examples
#' data(threeGaussians)
#' plot(threeGaussians, cex = 0.5, pch = 19);
#'
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#' @title Mixed Shapes Clusters
#'
#' @description Matrix of 2-dimensional points that form circular and elliptical
#' patterns.
#'
#' @name mixShapes
#'
#' @docType data
#'
#' @keywords datasets
#'
#' @examples
#' data(mixShapes)
#' plot(mixShapes, cex = 0.5, pch = 19);
#'
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#' @title T-Shaped Clusters
#'
#' @description Matrix of 2-dimensional points that form the letter T.
#'
#' @name Tset
#'
#' @docType data
#'
#' @keywords datasets
#'
#' @examples
#' data(Tset)
#' plot(Tset, cex = 0.5, pch = 19);
#'
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