UniversalCVI (version 1.2.0)

CCV.IDX: 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)

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.

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.

Author

Nathakhun Wiroonsri and Onthada Preedasawakul

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.

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)