R/clu_cmeans.R

In daltoolbox: Leveraging Experiment Lines to Data Analytics

#'@title Fuzzy c-means
#'@description Fuzzy c-means clustering using `e1071::cmeans`.
#'@details Produces soft membership for each cluster. The hard assignment is returned by `cluster()`.
#' Membership degrees are returned in the `membership` attribute.
#'@param centers number of clusters
#'@param m fuzziness parameter (m > 1)
#'@param iter maximum number of iterations
#'@param dist distance method passed to `e1071::cmeans`
#'@return returns a fuzzy clustering object.
#'@references
#' Bezdek, J. C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms.
#'@examples
#'data(iris)
#'model <- cluster_cmeans(centers = 3, m = 2)
#'model <- fit(model, iris[,1:4])
#'clu <- cluster(model, iris[,1:4])
#'table(clu)
#'@export
cluster_cmeans <- function(centers = 2, m = 2, iter = 100, dist = "euclidean") {
  obj <- clusterer()
  obj$centers <- centers
  obj$m <- m
  obj$iter <- iter
  obj$dist <- dist
  obj$model <- NULL
  class(obj) <- append("cluster_cmeans", class(obj))
  return(obj)
}

#'@importFrom e1071 cmeans
#'@exportS3Method fit cluster_cmeans
fit.cluster_cmeans <- function(obj, data, ...) {
  obj$model <- e1071::cmeans(
    data,
    centers = obj$centers,
    m = obj$m,
    iter.max = obj$iter,
    dist = obj$dist
  )
  return(obj)
}

#'@exportS3Method cluster cluster_cmeans
cluster.cluster_cmeans <- function(obj, data, ...) {
  if (is.null(obj$model)) {
    obj <- fit(obj, data)
  }
  model <- obj$model
  cluster <- model$cluster
  attr(cluster, "membership") <- model$membership
  if (!is.null(model$withinerror)) {
    attr(cluster, "metric") <- model$withinerror
  }
  return(cluster)
}