# Clustering Algorithm with a Greedy Agglomerative Heuristic and Special Distance Measures
## 2 Kohonen Neural Networks for Clustering Problem
### Proposed Algorithms
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**BVQ**} & \multirow{2}{*}{**SOM**} & \multicolumn{2}{c}{**BVQ-**} & \multicolumn{2}{c}{**BVQ-**} & \multicolumn{2}{c}{**SOM-**} & \multicolumn{2}{c}{**SOM-**} \\  & & & **GREEDY(2)** & **GREEDY(3)** & **GREEDY(2)** & **GREEDY(3)** \\ \hline \multicolumn{6}{c}{Two-batch mixed lot} \\  & \multicolumn{2}{c}{\(KI=2\)} & \multicolumn{2}{c}{\(KI=4\)} & \(KI=6\) & \(KI=4\) & \(KI=6\) \\ Min & 207.903 & 195.096 & 204.323 & 204.113 & 205.657 & 206.582 \\ Max & 207.903 & 218.493 & 214.846 & 216.201 & 251.462 & 249.768 \\ Mean & 207.903 & 206.037 & 205.904 & 209.754 & 225.213 & 230.583 \\ \(\sigma\) & 0.000 & 10.063 & 3.660 & 6.242 & 19.539 & 18.841 \\ V & 0.000 & 4.884 & 1.778 & 2.976 & 8.676 & 8.171 \\ R & 0.000 & 23.396 & 10.523 & 12.088 & 45.805 & 43.187 \\ \multicolumn{6}{c}{Three-batch mixed lot} \\  & \multicolumn{2}{c}{\(KI=3\)} & \multicolumn{2}{c}{\(KI=6\)} & \(KI=9\) & \(KI=6\) & \(KI=9\) \\ Min & 258.930 & 252.251 & 258.930 & 254.905 & 357.439 & 254.905 \\ Max & 366.993 & 347.509 & 366.993 & 364.023 & 366.993 & 364.023 \\ Mean & 357.453 & 315.496 & 348.951 & 336.351 & 365.296 & 333.542 \\ \(\sigma\) & 28.002 & 44.784 & 35.296 & 44.964 & 3.362 & 49.308 \\ V & 7.834 & 14.195 & 10.115 & 13.368 & 0.920 & 14.783 \\ R & 108.063 & 95.259 & 108.063 & 109.118 & 9.554 & 109.118 \\ \multicolumn{6}{c}{Four-batch mixed lot} \\  & \multicolumn{2}{c}{\(KI=4\)} & \multicolumn{2}{c}{\(KI=8\)} & \(KI=12\) & \(KI=8\) & \(KI=12\) \\ Min & 552.568 & 497.795 & 493.396 & 493.368 & 556.048 & 552.696 \\ Max & 698.601 & 620.549 & 561.092 & 561.078 & 705.538 & 702.573 \\ Mean & 601.527 & 533.155 & 531.922 & 513.349 & 583.717 & 614.007 \\ \(\sigma\) & 60.196 & 38.032 & 32.671 & 29.471 & 49.765 & 62.183 \\ V & 10.007 & 7.133 & 6.142 & 5.741 & 8.525 & 10.127 \\ R & 146.033 & 122.754 & 67.696 & 67.710 & 149.490 & 149.877 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Objective function value summarized after 30 attempts (Chebyshev distance, initialization by random). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**BVQ**} & \multirow{2}{*}{**SOM**} & \multicolumn{2}{c}{**BVQ-**} & \multicolumn{2}{c}{**BVQ-**} & \multicolumn{2}{c}{**SOM-**} & \multicolumn{2}{c}{**SOM-**} \\  & & & **GREEDY(2)** & **GREEDY(3)** & **GREEDY(2)** & **GREEDY(3)** \\ \hline \multicolumn{6}{c}{Two-batch mixed lot} \\  & \multicolumn{2}{c}{\(KI=2\)} & \multicolumn{2}{c}{\(KI=4\)} & \(KI=6\) & \(KI=4\) & \(KI=6\) \\ Min & 186.160 & 185.954 & 186.346 & 186.372 & 186.346 & 186.372 \\ Max & 186.160 & 186.211 & 186.346 & 186.372 & 186.346 & 186.372 \\ Mean & 186.160 & 186.100 & 186.346 & 186.372 & 186.346 & 186.372 \\ \(\sigma\) & 0.000 & 0.062 & 0.000 & 0.000 & 0.000 \\ V & 0.000 & 0.033 & 0.000 & 0.000 & 0.000 \\ R & 0.000 & 0.258 & 0.000 & 0.000 & 0.000 \\ \multicolumn{6}{c}{Three-batch mixed lot} \\  & \multicolumn{2}{c}{\(KI=3\)} & \multicolumn{2}{c}{\(KI=6\)} & \(KI=9\) & \(KI=6\) & \(KI=9\) \\ Min & 314.924 & 241.634 & 314.924 & 310.298 & 314.924 & 310.298 \\ Max & 314.924 & 312.797 & 325.737 & 320.973 & 325.737 & 320.973 \\ Mean & 314.924 & 260.991 & 315.645 & 313.145 & 315.645 & 311.722 \\ \(\sigma\) & 0.000 & 31.661 & 2.792 & 4.886 & 2.792 & 3.756 \\ V & 0.000 & 12.131 & 0.885 & 1.560 & 0.885 & 1.205 \\ R & 0.000 & 71.163 & 10.813 & 10.675 & 10.813 & 10.675 \\ \multicolumn{6}{c}{Four-batch mixed lot} \\  & \multicolumn{2}{c}{\(KI=4\)} & \multicolumn{2}{c}{\(KI=8\)} & \(KI=12\) & \(KI=8\) & \(KI=12\) \\ Min & 703.485 & 492.661 & 711.930 & 699.061 & 711.945 & 707.793 \\ Max & 703.485 & 550.607 & 711.945 & 707.793 & 711.945 & 707.793 \\ Mean & 703.485 & 528.546 & 711.944 & 707.096 & 711.945 & 707.793 \\ \(\sigma\) & 0.000 & 25.546 & 0.004 & 2.242 & 0.000 & 0.000 \\ V & 0.000 & 4.833 & 0.001 & 0.317 & 0.000 & 0.000 \\ R & 0.000 & 57.946 & 0.015 & 8.732 & 0.000 & 0.000 \\ \hline \hline \end{tabular}
\end{table}