STRPBM.IDX {UniversalCVI}

Starczewski and Pakhira-Bandyopadhyay-Maulik for crisp clustering indexes

Description

Computes the STR (A. Starczewski, 2017) and PBM (M. K. Pakhira et al., 2004) indexes for a result either kmeans or hierarchical clustering from user specified kmin to kmax.

Usage

STRPBM.IDX(x, kmax, kmin = 2, method = "kmeans", indexlist = "all", 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".

indexlist

a character string indicating which cluster validity indexes to be computed ("all", "STR", "PBM"). More than one indexes can be selected.

nstart

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

Details

PBM index can be used with both crisp and fuzzy clustering algorithms.
The largest value of STR(k) indicates a valid optimal partition.
The largest value of PBM(k) indicates a valid optimal partition.

Value

STR

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

PBM

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

Author(s)

Nathakhun Wiroonsri and Onthada Preedasawakul

References

M. K. Pakhira, S. Bandyopadhyay and U. Maulik, "Validity index for crisp and fuzzy clusters," Pattern Recogn 37(3):487–501 (2004).

A. Starczewski, "A new validity index for crisp clusters," Pattern Anal Applic 20, 687–700 (2017).

See Also

Wvalid, FzzyCVIs, DI.IDX, R1_data

Examples

library(UniversalCVI)

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

# ---- Kmeans ----

# Compute all the indices by STRPBM.IDX
K.ALL = STRPBM.IDX(scale(x), kmax = 15, kmin = 2, method = "kmeans",
  indexlist = "all", nstart = 100)
print(K.ALL)

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

# ---- Hierarchical ----

# Average linkage

# Compute all the indices by STRPBM.IDX
H.ALL = STRPBM.IDX(scale(x), kmax = 15, kmin = 2, method = "hclust_average",
  indexlist = "all")
print(H.ALL)

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