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The hierarchical clustering tree of 15 AIFSs , -tterm from the equivalent matrix T4 obtained by improved IFS(

Figure 2 The hierarchical clustering tree of 15 AIFSs , -tterm from the equivalent matrix T4 obtained by improved IFS(

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continuing this process for each t < i € N we have:
continuing this process for each t < i € N we have:
Table 1: Existing similarity measures between the AIF'Ss
Table 1: Existing similarity measures between the AIF'Ss
Note: ”I, A” represcats that the degree of similarity can’t be calculated because of ” division by zero problem” and bol entities notes unreasonable results. Table 4: A comparative outputs of the pattern recognition problem discussed in Example 3 with the proposed Sho. and the other existing similarity measures (p = 1 andt = 2in Spa; p=1in Spc, Su,Szrsi,S1is2; p = 1 and wi = 1/3 in Soc; p= 1, wm = 1/3 and wi = 1/3 in Spg3; wi = 1/3 in Syz).
Note: ”I, A” represcats that the degree of similarity can’t be calculated because of ” division by zero problem” and bol entities notes unreasonable results. Table 4: A comparative outputs of the pattern recognition problem discussed in Example 3 with the proposed Sho. and the other existing similarity measures (p = 1 andt = 2in Spa; p=1in Spc, Su,Szrsi,S1is2; p = 1 and wi = 1/3 in Soc; p= 1, wm = 1/3 and wi = 1/3 in Spg3; wi = 1/3 in Syz).
Table 5: A comparative outputs of the pattern recognition problem discussed in Example 4 with the pre »osed Sho. and the other existin; similarity measures (p = 1 andt = 2in Spy; p=1in Spc, Sy,Szs1,Sis2} p= land wj=1/6 in Soc, 9=1, wm = 1/3 and w; = 1/ in Spg3; wi = 1/6 in Syz).
Table 5: A comparative outputs of the pattern recognition problem discussed in Example 4 with the pre »osed Sho. and the other existin; similarity measures (p = 1 andt = 2in Spy; p=1in Spc, Sy,Szs1,Sis2} p= land wj=1/6 in Soc, 9=1, wm = 1/3 and w; = 1/ in Spg3; wi = 1/6 in Syz).
Note: ”N __’ repre. its that the degree of similarity can’t be calculated because of ” division by zero problem” and bol entities ‘enotes unreasonable results. Table 6: A comparative results of the proposed similarity measures with the other existing similarity measures for medical diagnosis (p =landt=2 in Spa; p=1in Spc, Su,Szs1,S1is2; p = 1 and w; = 1/5 in Sco; p= 1, wm = 1/3 and w; = 1/5 in Szg3; wi = 1/5 in Syz).
Note: ”N __’ repre. its that the degree of similarity can’t be calculated because of ” division by zero problem” and bol entities ‘enotes unreasonable results. Table 6: A comparative results of the proposed similarity measures with the other existing similarity measures for medical diagnosis (p =landt=2 in Spa; p=1in Spc, Su,Szs1,S1is2; p = 1 and w; = 1/5 in Sco; p= 1, wm = 1/3 and w; = 1/5 in Szg3; wi = 1/5 in Syz).
No.-:”N/A” represents that the degree of similarity can’t be calculated because of division by zero problem and »old entities denotes unreasonable results.
No.-:”N/A” represents that the degree of similarity can’t be calculated because of division by zero problem and »old entities denotes unreasonable results.
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Information SystemsMathematicsApplied MathematicsComputer ScienceArtificial IntelligenceSimilarity Geometry
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