SF.IDX {UniversalCVI}

The score function

Description

Computes the SF (S. Saitta et al., 2007) index for a result either kmeans or hierarchical clustering from user specified kmin to kmax.

Usage

SF.IDX(x, kmax, kmin = 2, method = "kmeans", nstart = 100)

Arguments

x

a numeric data frame or matrix where each column is a variable to be used for cluster analysis and each row is a data point.

kmax

a maximum number of clusters to be considered.

kmin

a minimum number of clusters to be considered. The default is 2.

method

a character string indicating which clustering method to be used ("kmeans", "hclust_complete", "hclust_average", "hclust_single"). The default is "kmeans".

nstart

a maximum number of initial random sets for kmeans for method = "kmeans". The default is 100.

Details

The smallest value of SF(k) indicates a valid optimal partition.

Value

SF

the Score function index for k from kmin to kmax shown in a data frame where the first and the second columns are k and the SF index, respectively.

Author(s)

Nathakhun Wiroonsri and Onthada Preedasawakul

References

S. Saitta, B. Raphael, I. Smith, "A bounded index for cluster validity," In Perner, P.: Machine Learning and Data Mining in Pattern Recognition, Lecture Notes in Computer Science, 4571, Springer (2007).

See Also

Hvalid, Wvalid, DI.IDX, FzzyCVIs, R1_data

Examples

library(UniversalCVI)

# The data is from Wiroonsri (2024).
x = R1_data[,1:2]

# ---- Kmeans ----

# Compute the SF index
K.SF = SF.IDX(scale(x), kmax = 15, kmin = 2, method = "kmeans", nstart = 100)
print(K.SF)

# The optimal number of cluster
K.SF[which.min(K.SF$SF),]

# ---- Hierarchical ----

# Average linkage

# Compute the SF index
H.SF = SF.IDX(scale(x), kmax = 15, kmin = 2, method = "hclust_average")
print(H.SF)

# The optimal number of cluster
H.SF[which.min(H.SF$SF),]