Table 4: Optimal solutions for the full Morse code dissimilarity matrix and the 35 35 food-item dissimilarity matrix.

Morse code full Food item data

1 (E) 0.00000 orange (3) 0.00000
2 (T) − 5.83333 watermelon (2) 0.28571
3 (I) 24.77778 apple (1) 0.28571
4 (A) 34.08333 banana (4) 1.28571
5 (N) 44.11111 pineapple (5) 1.62857
6 (M) − − 52.33333 lettuce (6) 21.25714
7 (S) 70.30556 broccoli (7) 22.31429
8 (U) 83.13889 carrots (8) 22.54286
9 (R) 93.47222 corn (9) 22.88571
10 (W) 102.97222 onions (10) 25.11429
11 (H) 114.44444 potato (11) 29.97143
12 (D) 124.30556 rice (12) 51.62857
13 (K) 131.52778 spaghetti (19) 58.97143
14 (V) 145.00000 bread (13) 64.48571
15 (5) 152.44444 bagel (14) 69.85714
16 (4) 159.91667 cereal (16) 72.11429
17 (F) 170.75000 oatmeal (15) 72.51429
18 (L) 182.25000 pancake (18) 76.37143
19 (B) 189.52778 muffin (17) 77.80000
20 (X) 199.55556 crackers (20) 88.97143
21 (6) 205.66667 granola bar (21) 93.00000
22 (3) 220.13889 pretzels (22) 100.74286
23 (C) 229.47222 nuts (24) 104.37143
24 (Y) 238.41667 popcorn (23) 107.62857
25 (7) 249.30556 potato chips (25) 112.48571
26 (Z) 254.88889 doughnuts (26) 120.08571
27 (Q) 264.38889 pizza (31) 126.51429
28 (P) 270.83333 cookies (27) 136.88571
29 (J) 282.94444 chocolate bar (29) 139.40000
30 (G) 292.47222 cake (28) 141.05714
31 (O) − − − 300.22222 pie (30) 143.54286
32 (2) 310.50000 ice cream (32) 152.00000
33 (8) 320.36111 yogurt (33) 157.11429
34 (1) 333.50000 butter (34) 161.80000
35 (9) 341.86111 cheese (35) 165.08571
36 (0) 350.27778