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,
...
)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 casehc_metricis ignored andkmust 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,kcan 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 producesNAvalues,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
xis 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
methodandSE.factorparameters passed tomaxSE. The default islist(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.
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 $avg.width (average width of all clusters)
size: the size of clusters
data: a matrix containing the original or the standardized data (if stand = TRUE)
The "eclust" class has methods for fviz_silhouette(), fviz_dend(), and fviz_cluster().
Author
Alboukadel Kassambara alboukadel.kassambara@gmail.com
Examples
# Load and scale data
data("USArrests")
df <- scale(USArrests)
# Enhanced k-means clustering
# nboot >= 500 is recommended
res.km <- eclust(df, "kmeans", nboot = 2)
# Silhouette plot
fviz_silhouette(res.km)
#> cluster size ave.sil.width
#> 1 1 7 0.27
#> 2 2 13 0.23
#> 3 3 11 0.39
#> 4 4 19 0.20
# Optimal number of clusters using gap statistics
res.km$nbclust
#> [1] 4
# Print result
res.km
#> K-means clustering with 4 clusters of sizes 7, 13, 11, 19
#>
#> Cluster means:
#> Murder Assault UrbanPop Rape
#> 1 -0.6958674 -0.5679476 1.12728218 -0.55096728
#> 2 -0.2162425 -0.2611064 -0.04793489 -0.06172647
#> 3 -1.1034717 -1.1654231 -0.99194587 -1.04874074
#> 4 1.0431796 1.0626143 0.19176752 0.85238754
#>
#> Clustering vector:
#> Alabama Alaska Arizona Arkansas California
#> 4 4 4 2 4
#> Colorado Connecticut Delaware Florida Georgia
#> 4 1 2 4 4
#> Hawaii Idaho Illinois Indiana Iowa
#> 1 3 4 2 3
#> Kansas Kentucky Louisiana Maine Maryland
#> 2 2 4 3 4
#> Massachusetts Michigan Minnesota Mississippi Missouri
#> 1 4 3 4 2
#> Montana Nebraska Nevada New Hampshire New Jersey
#> 3 2 4 3 1
#> New Mexico New York North Carolina North Dakota Ohio
#> 4 4 4 3 2
#> Oklahoma Oregon Pennsylvania Rhode Island South Carolina
#> 2 2 1 1 4
#> South Dakota Tennessee Texas Utah Vermont
#> 3 4 4 1 3
#> Virginia Washington West Virginia Wisconsin Wyoming
#> 2 2 3 3 2
#>
#> Within cluster sum of squares by cluster:
#> [1] 5.244931 10.860162 8.499862 45.263622
#> (between_SS / total_SS = 64.4 %)
#>
#> Available components:
#>
#> [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
#> [6] "betweenss" "size" "iter" "ifault" "clust_plot"
#> [11] "silinfo" "nbclust" "data" "gap_stat"
if (FALSE) { # \dontrun{
# 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
} # }