CCV.IDX {UniversalCVI}

Correlation Cluster Validity (CCV) index

Description

Computes the CCVP and CCVS (M. Popescu et al., 2013) indexes for a result of either FCM or EM clustering from user specified cmin to cmax.

Usage

CCV.IDX(x, cmax, cmin = 2, indexlist = "all", method = 'FCM', fzm = 2,
  iter = 100, nstart = 20)

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.

cmax

a maximum number of clusters to be considered.

cmin

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

indexlist

a character string indicating which The generalized C index be computed ("all","CCVP","CCVS"). More than one indexes can be selected.

method

a character string indicating which clustering method to be used ("FCM" or "EM"). The default is "FCM".

fzm

a number greater than 1 giving the degree of fuzzification for method = "FCM". The default is 2.

iter

a maximum number of iterations for method = "FCM". The default is 100.

nstart

a maximum number of initial random sets for FCM for method = "FCM". The default is 20.

Details

A new cluster validity framework that compares the structure in the data to the structure of dissimilarity matrices induced by a matrix transformation of the partition being tested. The largest value of CCV(c) indicates a valid optimal partition.

Value

Each of the followings shows the values of each index for c from cmin to cmax in a data frame.

CCVP

the Pearson Correlation Cluster Validity index.

CCVS

the Spearman’s (rho) Correlation Cluster Validity index.

Author(s)

Nathakhun Wiroonsri and Onthada Preedasawakul

References

M. Popescu, J. C. Bezdek, T. C. Havens and J. M. Keller (2013). "A Cluster Validity Framework Based on Induced Partition Dissimilarity." https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6246717&isnumber=6340245

See Also

R1_data, TANG.IDX, FzzyCVIs, WP.IDX, Hvalid

Examples

library(UniversalCVI)

# Iris data
x = iris[,1:4]

# ---- FCM algorithm ----


# Compute all the indices by CCV.IDX
FCM.ALL.CCV = CCV.IDX(scale(x), cmax = 10, cmin = 2, indexlist = "all",
  method =  'FCM', fzm = 2, iter = 100, nstart = 20)
print(FCM.ALL.CCV)

# Compute CCVP index
FCM.CCVP = CCV.IDX(scale(x), cmax = 10, cmin = 2, indexlist = "CCVP",
  method =  'FCM', fzm = 2, iter = 100, nstart = 20)
print(FCM.CCVP)


# ---- EM algorithm ----

# Compute all the indices by CCV.IDX
EM.ALL.CCV = CCV.IDX(scale(x), cmax = 10, cmin = 2, indexlist = "all",
  method =  'EM', iter = 100, nstart = 20)
print(EM.ALL.CCV)

# Compute CCVP index
EM.CCVP = CCV.IDX(scale(x), cmax = 10, cmin = 2, indexlist = "CCVP",
  method =  'EM', iter = 100, nstart = 20)
print(EM.CCVP)