# 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 \hline \multicolumn{7}{c}{Two-batch mixed lot} \\  & \(KI=2\) & \(KI=4\) & \(KI=6\) & \(KI=4\) & \(KI=6\) \\ Min & 207.903 & 195.429 & 204.323 & 204.113 & 205.657 & 216.201 \\ Max & 207.903 & 217.037 & 335.401 & 337.849 & 335.401 & 337.849 \\ Mean & 207.903 & 204.757 & 230.805 & 320.018 & 270.697 & 307.738 \\ \(\sigma\) & 0.000 & 9.869 & 54.136 & 47.057 & 44.573 & 43.024 \\ V & 0.000 & 4.820 & 23.455 & 14.705 & 16.466 & 13.981 \\ R & 0.000 & 21.608 & 131.079 & 133.737 & 129.744 & 121.648 \\ \multicolumn{7}{c}{Three-batch mixed lot} \\  & \(KI=3\) & \(KI=6\) & \(KI=9\) & \(KI=6\) & \(KI=9\) \\ Min & 258.930 & 252.467 & 294.450 & 303.473 & 258.930 & 254.905 \\ Max & 258.930 & 347.462 & 366.993 & 365.509 & 366.993 & 366.993 \\ Mean & 258.930 & 328.184 & 349.165 & 351.103 & 302.155 & 335.717 \\ \(\sigma\) & 0.000 & 37.900 & 19.111 & 21.056 & 54.798 & 50.449 \\ V & 0.000 & 11.548 & 5.473 & 5.997 & 18.136 & 15.027 \\ R & 0.000 & 94.995 & 72.543 & 62.036 & 108.063 & 112.088 \\ \multicolumn{7}{c}{Four-batch mixed lot} \\  & \(KI=4\) & \(KI=8\) & \(KI=12\) & \(KI=8\) & \(KI=12\) \\ Min & 554.632 & 495.630 & 493.396 & 566.491 & 555.149 & 692.980 \\ Max & 703.198 & 664.941 & 719.547 & 719.547 & 732.274 & 878.714 \\ Mean & 648.439 & 557.872 & 589.796 & 625.305 & 690.896 & 721.950 \\ \(\sigma\) & 66.907 & 55.821 & 82.804 & 73.941 & 53.478 & 47.524 \\ V & 10.318 & 10.006 & 14.039 & 11.825 & 7.740 & 6.583 \\ R & 148.566 & 169.311 & 226.151 & 153.056 & 177.125 & 185.735 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \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}{\(K1=2\)} & \multicolumn{2}{c}{\(KI=4\)} & \(KI=6\) & \(KI=4\) & \(KI=6\) \\ Min & 186.160 & 185.969 & 186.346 & 186.372 & 186.346 & 186.372 \\ Max & 186.160 & 186.207 & 186.346 & 186.372 & 186.346 & 186.372 \\ Mean & 186.160 & 186.087 & 186.346 & 186.372 & 186.346 & 186.372 \\ \(\sigma\) & 0.000 & 0.061 & 0.000 & 0.000 & 0.000 & 0.000 \\ V & 0.000 & 0.033 & 0.000 & 0.000 & 0.000 & 0.000 \\ R & 0.000 & 0.237 & 0.000 & 0.000 & 0.000 & 0.000 \\ \multicolumn{6}{c}{Three-batch mixed lot} \\  & \multicolumn{2}{c}{\(KI=4\)} & \multicolumn{2}{c}{\(KI=8\)} & \(KI=12\) & \(KI=8\) & \(KI=12\) \\ Min & 546.596 & 492.597 & 492.933 & 492.903 & 552.883 & 548.804 \\ Max & 546.596 & 655.370 & 549.751 & 492.903 & 552.883 & 548.804 \\ Mean & 546.596 & 521.356 & 541.720 & 492.903 & 552.883 & 548.803 \\ \(\sigma\) & 0.000 & 45.038 & 19.809 & 0.000 & 0.000 & 0.001 \\ V & 0.000 & 8.639 & 3.657 & 0.000 & 0.000 & 0.000 \\ R & 0.000 & 162.772 & 56.818 & 0.000 & 0.000 & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Objective function value summarized after 30 attempts (Euclidean distance, initialization by average).