fviz_silhouette {factoextra} | R Documentation |
Silhouette (Si) analysis is a cluster validation approach that
measures how well an observation is clustered and it estimates the average
distance between clusters. fviz_silhouette() provides ggplot2-based elegant
visualization of silhouette information from i) the result of
silhouette
(), pam
(),
clara
() and fanny
() [in
cluster package]; ii) eclust
() and hcut
() [in
factoextra].
Read more: Clustering Validation Statistics.
fviz_silhouette(sil.obj, label = FALSE, print.summary = TRUE, ...)
sil.obj |
an object of class silhouette: pam, clara, fanny [in cluster package]; eclust and hcut [in factoextra]. |
label |
logical value. If true, x axis tick labels are shown |
print.summary |
logical value. If true a summary of cluster silhouettes are printed in fviz_silhouette(). |
... |
other arguments to be passed to the function ggpubr::ggpar(). |
- Observations with a large silhouhette Si (almost 1) are very well clustered.
- A small Si (around 0) means that the observation lies between two clusters.
- Observations with a negative Si are probably placed in the wrong cluster.
return a ggplot
Alboukadel Kassambara alboukadel.kassambara@gmail.com
fviz_cluster
, hcut
,
hkmeans
, eclust
, fviz_dend
set.seed(123) # Data preparation # +++++++++++++++ data("iris") head(iris) # Remove species column (5) and scale the data iris.scaled <- scale(iris[, -5]) # K-means clustering # +++++++++++++++++++++ km.res <- kmeans(iris.scaled, 3, nstart = 2) # Visualize kmeans clustering fviz_cluster(km.res, iris[, -5], ellipse.type = "norm")+ theme_minimal() # Visualize silhouhette information require("cluster") sil <- silhouette(km.res$cluster, dist(iris.scaled)) fviz_silhouette(sil) # Identify observation with negative silhouette neg_sil_index <- which(sil[, "sil_width"] < 0) sil[neg_sil_index, , drop = FALSE] ## Not run: # PAM clustering # ++++++++++++++++++++ require(cluster) pam.res <- pam(iris.scaled, 3) # Visualize pam clustering fviz_cluster(pam.res, ellipse.type = "norm")+ theme_minimal() # Visualize silhouhette information fviz_silhouette(pam.res) # Hierarchical clustering # ++++++++++++++++++++++++ # Use hcut() which compute hclust and cut the tree hc.cut <- hcut(iris.scaled, k = 3, hc_method = "complete") # Visualize dendrogram fviz_dend(hc.cut, show_labels = FALSE, rect = TRUE) # Visualize silhouhette information fviz_silhouette(hc.cut) ## End(Not run)