simNSCA {ipsfs}

IFS similarity measure simNSCA

Description

IFS similarity measure values using simNSCA computation technique with membership, and non-membership values of two objects or set of objects.

Usage

simNSCA(ma, na, mb, nb, k)

Arguments

ma

IFS membership values for the data set x computed using either triangular or trapezoidal or guassian membership function

na

IFS non-membership values for the data set x computed using either Sugeno and Terano's or Yager's non-membership function

mb

IFS membership values for the data set y computed using either triangular or trapezoidal or guassian membership function

nb

IFS non-membership values for the data set y computed using either Sugeno and Terano's or Yager's non-membership function

k

A constant value, considered as 1

Value

The IFS similarity values of data set y with data set x

References

R. T. Ngan, B. C. Cuong, M. Ali, et al. H-max distance measure of intuitionistic fuzzy sets in decision making. Applied Soft Computing, 69:393 - 425, 2018.

Examples

x<-matrix(c(12,9,14,11,21,16,15,24,20,17,14,11),nrow=4)
y<-matrix(c(11,21,6),nrow=1)
a<-mn(x)
b<-std(x)
y<-matrix(c(11,24,21,12,6,11,15,21),nrow=1)
a1<-mn(y)
b1<-std(y)
lam<-0.5
ma<-memG(a,b,x)
na<-nonmemS(ma,lam)
mb<-memG(a1,b1,y)
nb<-nonmemS(mb,lam)
k<-1
simNSCA(ma,na,mb,nb,k)
#[1] 0.6928792 0.6934970 0.8754130 0.8754130