simDC: IFS similarity measure simDC

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

IFS similarity measure simDC

Description

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

Usage

simDC(ma, na, mb, nb, ha, hb, p, 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
ha	IFS hesitancy values for the data set x
hb	IFS hesitancy values for the data set y
p	Lp norm values for measuring the p-norm distance between x and y, values range from 1 to 5
k	A constant value, considered as 1

Value

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

References

L. Dengfeng and C. Chuntian. New similarity measures of intuitionistic fuzzy sets and application to pattern recognitions. Pattern recognition letters, 23(1-3):221 - 225, 2002.

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)
ha<-hmemIFS(ma,na)
mb<-memG(a1,b1,y)
nb<-nonmemS(mb,lam)
hb<-hmemIFS(mb,nb)
p<-2
k<-1
simDC(ma,na,mb,nb,ha,hb,p,k)
#[1] 0.3553975 0.3558802 0.5378438 0.5378438

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