simHK: IFS similarity measure simHK

In ipsfs: Intuitionistic, Pythagorean, and Spherical Fuzzy Similarity Measure

IFS similarity measure simHK

Description

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

Usage

simHK(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

D. H. Hong and C. Kim. A note on similarity measures between vague sets and between elements. Information sciences, 115(1-4):83 - 96, 1999.

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)
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
simHK(ma,na,mb,nb,k)
#[1] 0.9702837 0.9702706 0.9874349 0.9874349

ipsfs documentation built on June 21, 2022, 5:07 p.m.