## 2 Methods
### CUDA Implementation of Greedy Agglomerative Procedures
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm or**} & \multicolumn{4}{c}{**Achieved Objective Function Values (1) Summarized after 30 Runs**} \\ \cline{2-6}  & **Min (Record)** & **Max (Worst)** & **Average** & **Median** & **Std. Dev** \\ \hline Lloyd (multistart) & 6.28454 \(\times\) 10\({}^{6}\) & 7.25139 \(\times\) 10\({}^{6}\) & 6.92349 \(\times\) 10\({}^{6}\) & 6.95864 \(\times\) 10\({}^{6}\) & 198,384 \\ j-means (SWAP\({}_{1}\) + Lloyd) & 3.76240 \(\times\) 10\({}^{6}\) & 4.00651 \(\times\) 10\({}^{6}\) & 3.85266 \(\times\) 10\({}^{6}\) & 3.84859 \(\times\) 10\({}^{6}\) & 59,481.8 \\ AGGL\({}_{1}\) & 4.17611 \(\times\) 10\({}^{6}\) & 5.71925 \(\times\) 10\({}^{6}\) & 4.87105 \(\times\) 10\({}^{6}\) & 4.79499 \(\times\) 10\({}^{6}\) & 417,612 \\ AGGL\({}_{2}\) & 3.64502 \(\times\) 10\({}^{6}\) & 3.67299 \(\times\) 10\({}^{6}\) & 3.65556 \(\times\) 10\({}^{6}\) & 3.65350 \(\times\) 10\({}^{6}\) & 7644.61 \\ AGGL\({}_{3}\) & 3.64508 \(\times\) 10\({}^{6}\) & 3.77578 \(\times\) 10\({}^{6}\) & 3.66037 \(\times\) 10\({}^{6}\) & 3.65225 \(\times\) 10\({}^{6}\) & 28,242.8 \\ AGGL\({}_{5}\) & 3.64502 \(\times\) 10\({}^{6}\) & 3.77250 \(\times\) 10\({}^{6}\) & 3.65956 \(\times\) 10\({}^{6}\) & 3.65221 \(\times\) 10\({}^{6}\) & 31,464.1 \\ AGGL\({}_{10}\) & 3.64503 \(\times\) 10\({}^{6}\) & 3.74222 \(\times\) 10\({}^{6}\) & 3.65639 \(\times\) 10\({}^{6}\) & 3.65191 \(\times\) 10\({}^{6}\) & 18,087.8 \\ AGGL\({}_{12}\) & 3.64508 \(\times\) 10\({}^{6}\) & 3.77121 \(\times\) 10\({}^{6}\) & 3.65745 \(\times\) 10\({}^{6}\) & 3.65299 \(\times\) 10\({}^{6}\) & 22,688.4 \\ AGGL\({}_{15}\) & 3.64351 \(\times\) 10\({}^{6}\) & 3.78305 \(\times\) 10\({}^{6}\) & 3.66115 \(\times\) 10\({}^{6}\) & 3.65050 \(\times\) 10\({}^{6}\) & 32,121.2 \\ AGGL\({}_{20}\) & 3.64289 \(\times\) 10\({}^{6}\) & 3.74971 \(\times\) 10\({}^{6}\) & 3.65333 \(\times\) 10\({}^{6}\) & 3.64965 \(\times\) 10\({}^{6}\) & 19,010.0 \\ AGGL\({}_{25}\) & 3.64354 \(\times\) 10\({}^{6}\) & 3.66605 \(\times\) 10\({}^{6}\) & 3.64913 \(\times\) 10\({}^{6}\) & 3.64784 \(\times\) 10\({}^{6}\) & 5184.35 \\ AGGL\({}_{30}\) & 3.64290 \(\times\) 10\({}^{6}\) & 3.67201 \(\times\) 10\({}^{6}\) & 3.65106 \(\times\) 10\({}^{6}\) & 3.64702 \(\times\) 10\({}^{6}\) & 7780.79 \\ AGGL\({}_{50}\) & 3.64511 \(\times\) 10\({}^{6}\) & 3.67594 \(\times\) 10\({}^{6}\) & 3.65120 \(\times\) 10\({}^{6}\) & 3.64940 \(\times\) 10\({}^{6}\) & 6920.69 \\ AGGL\({}_{75}\) & 3.64510 \(\times\) 10\({}^{6}\) & 3.68136 \(\times\) 10\({}^{6}\) & 3.65552 \(\times\) 10\({}^{6}\) & 3.65355 \(\times\) 10\({}^{6}\) & 7722.98 \\ AGGL\({}_{100}\) & 3.64508 \(\times\) 10\({}^{6}\) & 3.67524 \(\times\) 10\({}^{6}\) & 3.65651 \(\times\) 10\({}^{6}\) & 3.65395 \(\times\) 10\({}^{6}\) & 9106.84 \\ GH-VNS1 & 3.69265 \(\times\) 10\({}^{6}\) & 5.02091 \(\times\) 10\({}^{6}\) & 4.05038 \(\times\) 10\({}^{6}\) & 3.93463 \(\times\) 10\({}^{6}\) & 336,130 \\ GH-VNS2 & 3.64503 \(\times\) 10\({}^{6}\) & 3.66884 \(\times\) 10\({}^{6}\) & 3.65123 \(\times\) 10\({}^{6}\) & 3.65015 \(\times\) 10\({}^{6}\) & 6284.54 \\ GH-VNS3 & 3.64503 \(\times\) 10\({}^{6}\) & 3.67655 \(\times\) 10\({}^{6}\) & 3.65735 \(\times\) 10\({}^{6}\) & 3.65370 \(\times\) 10\({}^{6}\) & 9247.64 \\ SWAP\({}_{1}\) (the best of SWAP) & 3.68260 \(\times\) 10\({}^{6}\) & 3.79031 \(\times\) 10\({}^{6}\) & 3.74321 \(\times\) 10\({}^{6}\) & 3.74447 \(\times\) 10\({}^{6}\) & 25,846.1 \\ GA-1 POINT & 4.38348 \(\times\) 10\({}^{6}\) & 5.29721 \(\times\) 10\({}^{6}\) & 4.83383 \(\times\) 10\({}^{6}\) & 4.82569 \(\times\) 10\({}^{6}\) & 198,278 \\ GA-UNIFORM & 5.00518 \(\times\) 10\({}^{6}\) & 6.29115 \(\times\) 10\({}^{6}\) & 5.62017 \(\times\) 10\({}^{6}\) & 5.60439 \(\times\) 10\({}^{6}\) & 347,120 \\ Aggl-EA & 3.64285 \(\times\) 10\({}^{6}\) & 3.65052 \(\times\) 10\({}^{6}\) & 3.64473 \(\times\) 10\({}^{6}\) & 3.64502 \(\times\) 10\({}^{6}\) & 1467.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Comparative results for Individual Household Electric Power Consumption (IHEPC) dataset.