Kira (version 1.0.7)

silhouette: Silhouette method to determine the optimal number of clusters.

Description

Generates the silhouette graph and returns the ideal number of clusters in the k-means method.

Usage

silhouette(data, k.cluster = 2:10, plot = TRUE, cut = TRUE,
           title = NA, xlabel = NA, ylabel = NA, size = 1.1, grid = TRUE, 
           color = TRUE, savptc = FALSE, width = 3236, height = 2000,
           res = 300, casc = TRUE)

Value

k.ideal

Ideal number of clusters.

eve.si

Vector with averages of silhouette indices of cluster groups (si).

Arguments

data

Data with x and y coordinates.

k.cluster

Cluster numbers for comparison in the k-means method (default = 2:10).

plot

Indicates whether to plot the silhouette graph (default = TRUE).

cut

Indicates whether to plot the best cluster indicative line (default = TRUE).

title

Title of the graphic, if not set, assumes the default text.

xlabel

Names the X axis, if not set, assumes the default text.

ylabel

Names the Y axis, if not set, assumes the default text.

size

Size of points on the graph and line thickness (default = 1.1).

grid

Put grid on graph (default = TRUE).

color

Colored graphic (default = TRUE).

savptc

Saves the graph image to a file (default = FALSE).

width

Graphic image width when savptc = TRUE (defaul = 3236).

height

Graphic image height when savptc = TRUE (default = 2000).

res

Nominal resolution in ppi of the graphic image when savptc = TRUE (default = 300).

casc

Cascade effect in the presentation of the graphic (default = TRUE).

Author

Paulo Cesar Ossani

References

Anitha, S.; Metilda, M. A. R. Y. An extensive investigation of outlier detection by cluster validation indices. Ciencia e Tecnica Vitivinicola - A Science and Technology Journal, v. 34, n. 2, p. 22-32, 2019. doi: 10.13140/RG.2.2.26801.63848

Kaufman, L. and Rousseeuw, P. J. Finding Groups in Data: An Introduction to Cluster Analysis, New York: John Wiley & Sons. 1990.

Martinez, W. L.; Martinez, A. R.; Solka, J. Exploratory data analysis with MATLAB. 2nd ed. New York: Chapman & Hall/CRC, 2010. 499 p.

Rousseeuw P. J. Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis. Journal of Computational and Applied Mathematics, 20:53-65. 1987. doi: 10.1016/0377-0427(87)90125-7

Examples

data(iris) # data set

res <- silhouette(data = iris[,1:4], k.cluster = 2:10, cut = TRUE, 
                  plot = TRUE, title = NA, xlabel = NA, ylabel = NA, 
                  size = 1.1, grid = TRUE, savptc = FALSE, width = 3236, 
                  color = TRUE, height = 2000, res = 300, casc = TRUE)
             
res$k.ideal # number of clusters
res$eve.si  # vector with averages of si indices


res <- silhouette(data = iris[,1:4], k.cluster = 3, cut = TRUE, 
                  plot = TRUE, title = NA, xlabel = NA, ylabel = NA, 
                  size = 1.1, grid = TRUE, savptc = FALSE, width = 3236, 
                  color = TRUE, height = 2000, res = 300, casc = TRUE)
             
res$k.ideal # number of clusters
res$eve.si  # vector with averages of si indices