eclust: Visual enhancement of clustering analysis

Description

Provides solution for enhancing the workflow of clustering analyses and ggplot2-based elegant data visualization. Read more: Visual enhancement of clustering analysis.

Usage

eclust(
  x,
  FUNcluster = c("kmeans", "pam", "clara", "fanny", "hclust", "agnes", "diana"),
  k = NULL,
  k.max = 10,
  stand = FALSE,
  graph = TRUE,
  hc_metric = "euclidean",
  hc_method = "ward.D2",
  gap_maxSE = list(method = "firstSEmax", SE.factor = 1),
  nboot = 100,
  verbose = interactive(),
  seed = 123,
  ...
)

Value

Returns an object of class "eclust" containing the result of the standard function used (e.g., kmeans, pam, hclust, agnes, diana, etc.).

It includes also:

cluster: the cluster assignement of observations after cutting the tree
nbclust: the number of clusters
silinfo: the silhouette information of observations, including $widths (silhouette width values of each observation), $clus.avg.widths (average silhouette width of each cluster) and $avg.width (average width of all clusters)
size: the size of clusters
data: a matrix containing the original or the standardized data (if stand = TRUE)

The "eclust" class has method for fviz_silhouette(), fviz_dend(), fviz_cluster().

Arguments

x: numeric vector, data matrix or data frame
FUNcluster: a clustering function including "kmeans", "pam", "clara", "fanny", "hclust", "agnes" and "diana". Abbreviation is allowed.
k: the number of clusters to be generated. If NULL, the gap statistic is used to estimate the appropriate number of clusters. In the case of kmeans, k can be either the number of clusters, or a set of initial (distinct) cluster centers.
k.max: the maximum number of clusters to consider, must be at least two.
stand: logical value; default is FALSE. If TRUE, then the data will be standardized using the function scale(). Measurements are standardized for each variable (column), by subtracting the variable's mean value and dividing by the variable's standard deviation.
graph: logical value. If TRUE, cluster plot is displayed.
hc_metric: character string specifying the metric to be used for calculating dissimilarities between observations. Allowed values are those accepted by the function dist() [including "euclidean", "manhattan", "maximum", "canberra", "binary", "minkowski"] and correlation based distance measures ["pearson", "spearman" or "kendall"]. Used only when FUNcluster is a hierarchical clustering function such as one of "hclust", "agnes" or "diana".
hc_method: the agglomeration method to be used (?hclust): "ward.D", "ward.D2", "single", "complete", "average", ...
gap_maxSE: a list containing the parameters (method and SE.factor) for determining the location of the maximum of the gap statistic (Read the documentation ?cluster::maxSE).
nboot: integer, number of Monte Carlo ("bootstrap") samples. Used only for determining the number of clusters using gap statistic.
verbose: logical value. If TRUE, the result of progress is printed.
seed: integer used for seeding the random number generator.
...: other arguments to be passed to FUNcluster.

Author

Alboukadel Kassambara alboukadel.kassambara@gmail.com

Examples

Run this code

# Load and scale data
data("USArrests")
df <- scale(USArrests)

# Enhanced k-means clustering
# nboot >= 500 is recommended
res.km <- eclust(df, "kmeans", nboot = 2)
# Silhouette plot
fviz_silhouette(res.km)
# Optimal number of clusters using gap statistics
res.km$nbclust
# Print result
 res.km
 
if (FALSE) {
 # Enhanced hierarchical clustering
 res.hc <- eclust(df, "hclust", nboot = 2) # compute hclust
  fviz_dend(res.hc) # dendrogam
  fviz_silhouette(res.hc) # silhouette plot
}