eclust: Visual enhancement of clustering analysis

Description

Provides a convenient workflow for clustering analyses and ggplot2-based data visualization. When k = NULL, the gap statistic selects the number of clusters. Hierarchical backends may validly return k = 1; in that case eclust() returns a one-cluster result without silhouette information. Read more: Enhanced Cluster Analysis in R with factoextra's eclust().

Usage

eclust(
  x,
  FUNcluster = c("kmeans", "pam", "clara", "fanny", "hclust", "agnes", "diana",
    "hkmeans"),
  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 also includes:

cluster: the cluster assignment of observations after cutting the tree
nbclust: the number of clusters
silinfo: the silhouette information of observations, when available for solutions with at least two clusters, 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 methods for fviz_silhouette(), fviz_dend(), and fviz_cluster().

Arguments

x: numeric vector, data matrix or data frame. For hierarchical clustering (FUNcluster = "hclust", "agnes" or "diana"), a precomputed dissimilarity matrix (an object of class "dist") may be supplied directly; in that case hc_metric is ignored and k must be specified. This allows custom distances such as Bray-Curtis (e.g. vegan::vegdist(df, "bray")).
FUNcluster: a clustering function including "kmeans", "pam", "clara", "fanny", "hkmeans", "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. For hierarchical clustering, this automatic selection may return k = 1. 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. If scaling produces NA values, eclust() stops with a package-level error.
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". Ignored when x is already a "dist" object.
hc_method: the agglomeration method to be used (?hclust): "ward.D", "ward.D2", "single", "complete", "average", ...
gap_maxSE: a list containing the method and SE.factor parameters passed to maxSE. The default is list(method = "firstSEmax", SE.factor = 1).
nboot: integer, number of Monte Carlo ("bootstrap") samples. Used only for determining the number of clusters using the gap statistic.
verbose: logical value. If TRUE, progress information 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) # dendrogram
if (res.hc$nbclust > 1) fviz_silhouette(res.hc) # silhouette plot
}