PB.IDX {UniversalCVI}

Point biserial correlation (PB)

Description

Computes the PB (G. W. Miligan, 1980) index for a result either kmeans or hierarchical clustering from user specified kmin to kmax.

Usage

PB.IDX(x, kmax, kmin = 2, method = "kmeans", corr = "pearson", 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".

corr

a character string indicating which correlation coefficient is to be computed ("pearson", "kendall" or "spearman"). The default is "pearson".

nstart

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

Details

The largest value of PB(k) indicates a valid optimal partition.

Value

PB

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

Author(s)

Nathakhun Wiroonsri and Onthada Preedasawakul

References

G. W. Miligan, "An examination of the effect of six types of error perturbation on fifteen clustering algorithms," Psychometrika, 45, 325-342 (1980).

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 PB index
K.PB = PB.IDX(scale(x), kmax = 15, kmin = 2, method = "kmeans",
  corr = "pearson", nstart = 100)
print(K.PB)

# The optimal number of cluster
K.PB[which.max(K.PB$PB),]

# ---- Hierarchical ----

# Average linkage

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

# The optimal number of cluster
H.PB[which.max(H.PB$PB),]