R/c_KMeans.R

In egenn/rtemis: Machine Learning and Visualization

# c_KMEANS.R
# ::rtemis::
# 2016 E.D. Gennatas www.lambdamd.org

#' K-means Clustering
#'
#' Perform [K-means clustering](https://en.wikipedia.org/wiki/K-means_clustering) using `flexclust::cclust`
#'
#' @param x Input matrix / data.frame
#' @param x.test Testing set matrix / data.frame
#' @param k Integer: Number of clusters to get
#' @param dist Character: Distance measure to use: 'euclidean' or 'manhattan'
#' @param verbose Logical: If TRUE, print messages to screen
#' @param ... Additional parameters to pass to `flexclust::cclust`
#'
#' @author E.D. Gennatas
#' @family Clustering
#' @export

c_KMeans <- function(x,
                     x.test = NULL,
                     k = 2,
                     dist = "euclidean",
                     verbose = TRUE, ...) {
  # Intro ----
  start.time <- intro(verbose = verbose)
  clust.name <- "KMeans"

  # Dependencies ----
  dependency_check("flexclust")

  # Arguments ----
  if (missing(x)) {
    print(args(c_KMeans))
    stop("x is missing")
  }

  # Data ----
  .colnames <- if (is.null(colnames(x))) paste0("Feature_", seq_len(NCOL(x))) else (colnames(x))
  x <- as.data.frame(x)
  xnames <- colnames(x) <- .colnames

  # KMEANS ----
  if (verbose) msg2("Performing K-means Clustering with k = ", k, "...", sep = "")
  clust <- flexclust::cclust(x,
    k = k,
    dist = dist,
    method = "kmeans", ...
  )

  # Clusters ----
  clusters.train <- flexclust::clusters(clust)
  if (!is.null(x.test)) {
    clusters.test <- flexclust::clusters(clust, x.test)
  } else {
    clusters.test <- NULL
  }

  # Outro ----
  cl <- rtClust$new(
    clust.name = clust.name,
    k = k,
    xnames = xnames,
    clust = clust,
    clusters.train = clusters.train,
    clusters.test = clusters.test,
    parameters = list(
      k = k,
      dist = dist
    ),
    extra = list()
  )
  outro(start.time, verbose = verbose)
  cl
} # rtemis::c_KMEANS