Computes Hierarchical Clustering and Cut the Tree

Computes hierarchical clustering (hclust, agnes, diana) and cut the tree into k clusters. It also accepts correlation based distance measure methods such as "pearson", "spearman" and "kendall".

hcut(x, k = 2, isdiss = inherits(x, "dist"), hc_func = c("hclust",
  "agnes", "diana"), hc_method = "ward.D2", hc_metric = "euclidean",
  stand = FALSE, graph = FALSE, ...)

Arguments

x
a numeric matrix, numeric data frame or a dissimilarity matrix.
k
the number of clusters to be generated.
isdiss
logical value specifying wether x is a dissimilarity matrix.
hc_func
the hierarchical clustering function to be used. Default value is "hclust". Possible values is one of "hclust", "agnes", "diana". Abbreviation is allowed.
hc_method
the agglomeration method to be used (?hclust) for hclust() and agnes(): "ward.D", "ward.D2", "single", "complete", "average", ...
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"].
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, the dendrogram is displayed.
...
not used.

Value

an object of class "hcut" containing the result of the standard function used (read the documentation of hclust, agnes, diana).

It includes also:

  • cluster: the cluster assignement of observations after cutting the tree
  • nbclust: the number of clusters
  • silinfo: the silhouette information of observations (if k > 1)
  • size: the size of clusters
  • data: a matrix containing the original or the standardized data (if stand = TRUE)

See also

fviz_dend, hkmeans, eclust

Examples

data(USArrests)

# Compute hierarchical clustering and cut into 4 clusters
res <- hcut(USArrests, k = 4, stand = TRUE)

# Cluster assignements of observations
res$cluster
#>        Alabama         Alaska        Arizona       Arkansas     California 
#>              1              2              2              3              2 
#>       Colorado    Connecticut       Delaware        Florida        Georgia 
#>              2              3              3              2              1 
#>         Hawaii          Idaho       Illinois        Indiana           Iowa 
#>              3              4              2              3              4 
#>         Kansas       Kentucky      Louisiana          Maine       Maryland 
#>              3              3              1              4              2 
#>  Massachusetts       Michigan      Minnesota    Mississippi       Missouri 
#>              3              2              4              1              3 
#>        Montana       Nebraska         Nevada  New Hampshire     New Jersey 
#>              4              4              2              4              3 
#>     New Mexico       New York North Carolina   North Dakota           Ohio 
#>              2              2              1              4              3 
#>       Oklahoma         Oregon   Pennsylvania   Rhode Island South Carolina 
#>              3              3              3              3              1 
#>   South Dakota      Tennessee          Texas           Utah        Vermont 
#>              4              1              2              3              4 
#>       Virginia     Washington  West Virginia      Wisconsin        Wyoming 
#>              3              3              4              4              3 
# Size of clusters
res$size
#> [1]  7 12 19 12

# Visualize the dendrogram
fviz_dend(res, rect = TRUE)

# Visualize the silhouette
fviz_silhouette(res)
#>   cluster size ave.sil.width
#> 1       1    7          0.46
#> 2       2   12          0.29
#> 3       3   19          0.26
#> 4       4   12          0.43

# Visualize clusters as scatter plots
fviz_cluster(res)