Intuitionistic Fuzzy Clustering Algorithms - Intuitionistic Fuzzy Aggregation and Clustering - page 216

Step 4 Check whether we should stop the iterations:

3

d wE ( V i ( 0 ), V i ( 1 ))/ 3 = 0 . 088 > 0 . 005

i = 1

Since this value is not small enough, we continue the iterations as follows:

K = 1:

⎡
⎣

⎤
⎦

0 . 399 0 . 326 0 . 275

0 . 138 0 . 169 0 . 693

0 . 202 0 . 176 0 . 622

. . .

0 919 0 042 0 039

. . .

0 128 0 724 0 148

U ( 1 ) =

0 . 309 0 . 410 0 . 280

0 . 101 0 . 127 0 . 772

0 . 071 0 . 164 0 . 766

0 . 899 0 . 054 0 . 048

0 . 057 0 . 840 0 . 102

⎡

0 . 356 , 0 . 387 0 . 841 , 0 . 086 0 . 776 , 0 . 157

⎣

V ( 2 ) = 0 . 753 , 0 . 150 0 . 661 , 0 . 172 0 . 585 , 0 . 236

0 . 587 , 0 . 258 0 . 530 , 0 . 187 0 . 668 , 0 . 179

⎤

0 . 647 , 0 . 160 0 . 198 , 0 . 705 0 . 181 , 0 . 757

⎦

0 . 494 , 0 . 176 0 . 710 , 0 . 225 0 . 479 , 0 . 490

0 . 321 , 0 . 553 0 . 344 , 0 . 601 0 . 630 , 0 . 158

3

d wE ( V i ( 1 ), V i ( 2 ))

= 0 . 024 > 0 . 005

3

i = 1

K = 2:

⎡
⎣

⎤
⎦

. . .

0 388 0 335 0 277

0 . 086 0 . 105 0 . 809

0 . 140 0 . 127 0 . 733

0 . 932 0 . 035 0 . 034

0 . 104 0 . 785 0 . 111

U ( 2 ) =

0 . 298 0 . 422 0 . 280

0 . 064 0 . 082 0 . 854

0 . 110 0 . 245 0 . 645

0 . 894 0 . 056 0 . 050

0 . 074 0 . 813 0 . 113