rYWAASB (version 0.2)

nbclust: Data read and estimate the cluster number

Description

nbclust() Reads and prepares the data, and determine the optimum number of clusters using Average Silhouette Method by factoextra package. The average silhouette approach assesses the quality of clustering by evaluating how well each object fits within its cluster. A high average silhouette width signifies effective clustering. This method calculates the average silhouette for different values of k, and the optimal number of clusters (k) is the one that maximizes the average silhouette across a range of potential k values.

Usage

nbclust(datap, verbose = FALSE)

Value

Returns a data frame

Arguments

datap

The data set

verbose

If verbose = TRUE then some results are

Author

Ali Arminian

Details

The silhouette coefficient (SC) refers to a criterion to decide number of clusters.It is defined as follows. Though there are numerous methods determining number of clusters such as the gap statistic etc.

SC = maxKSK.

In other words, for each observation i, the silhouette width s(i) is defined as follows: Put a(i) = average dissimilarity between i and all other points of the cluster to which i belongs (if i is the only observation in its cluster, s(i):= 0 without further calculations). For all other clusters C, put d(i, C) = average dissimilarity of i to all observations of C. The smallest of these d(i, C) is b(i)=min(C) d(i, C), and can be seen as the dissimilarity between i and its “neighbor” cluster, i.e., the nearest one to which it does not belong. Finally, s(i)=:=b(i)-a(i)max(a(i), b(i))

• Note: The clustering methods can be: "average", "centroid", "complete", "mcquitty", "median", "single", "ward.D", "ward.D2" and, Distance methods can be as: "binary", "canberra", "euclidean", "manhattan", "minkowski", "maximum", "pearson", "spearman", "kendall" which may be used in shipunov or factoextra packages. In this package we just applied average=UPGMA and ward algorithms.

References

Lletı, R., Ortiz, M.C., Sarabia, L.A., Sánchez, M.S. 2004. Selecting variables for k-means cluster analysis by using a genetic algorithm that optimizes the silhouettes, Analytica Chimica Acta, 515(1): 87-100.

Rousseeuw, P.J. (1987) Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math., 20, 53-65.

https://uc-r.github.io/

Examples

# \donttest{
library(factoextra)

data(maize)
maize <- as.data.frame(maize)
row.names(maize) <- maize[, 1]
maize[, 1] = NULL
GEN <- row.names(maize)
maize <- scale(maize)
nbclust(maize, verbose = FALSE)

# Performing bootstrap or jackknife clustering
# by shipunov package. The examples should be run in the
# console manually due to problems occurs in the ORPHANED
# package `shipunov`.
#
# 1- Bootstrap clustering:
# data.jb <- Jclust(maize,
#   method.d = "euclidean",
#    method.c = "average", n.cl = 2,
#     bootstrap = TRUE)
#
# plot.Jclust(data.jb, top=TRUE, lab.pos=1,
#  lab.offset=1, lab.col=2, lab.font=2)
# Fence(data.jb$hclust, GEN)
#
# data.jb <- Jclust(maize,
#  method.d = "euclidean",
#    method.c = "ward.D", n.cl = 2,
#     bootstrap = TRUE)
#
# plot.Jclust(data.jb, top=TRUE, lab.pos=1,
#            lab.offset=1, lab.col=2, lab.font=2)
# Fence(data.jb$hclust, GEN)
#
# if(verbose = TRUE):
# cat("\nnumber of iterations:\n", data.jb$iter, "\n")
#
# for "bootstrap":
# data.jb$mat <- as.matrix((data.jb$mat))
# data.jb$mat
# cat("\nmatrix of results:\n", data.jb$mat, "\n")
# cat("clustering info, by eucledean distance measure:\n")
# print(data.jb$hclust)
# cat("groups:\n", data.jb$gr, "\n")
# cat("\nsupport values:\n", data.jb$supp, "\n")
# cat("\nnumber of clusters used:\n", data.jb$n.cl, "\n")

# 2- Jackknife clustering:
# data.jb <- Bclust(maize,
#   method.d = "euclidean", method.c = "average",
#    bootstrap = FALSE)
# plot(data.jb)
#
# data.jb <- Bclust(maize,
#   method.d = "euclidean", method.c = "ward.D",
#    bootstrap = FALSE)
# plot(data.jb)
#
# if(verbose = TRUE):
# For"jackknife":
# cat("Consensus:\n", data.jb$consensus, "\n")
# cat("Vlaues:\n", data.jb$values, "\n")
# }