## 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)**} & \multicolumn{1}{c}{**Summarized after 30 Runs**} \\ \cline{2-6}  & **Min (Record)** & **Max (Worst)** & **Average** & **Median** & **Std. Dev** \\ \hline Lloyd (multistart) & 49,901.8086 & 58,872.1094 & 53,113.4275 & 52,599.0273 & 2781.6693 \\ j-means (SWAP\({}_{1}\) + Lloyd) & 44,074.6445 & 47,562.4922 & 45,375.1657 & 44,608.1953 & 1505.4071 \\ AGGL\({}_{1}\) & 67,508.2344 & 69,943.5000 & 69,070.9118 & 69,328.3750 & 845.3473 \\ AGGL\({}_{2}\) & 43,966.8594 & 67,854.2500 & 60,923.9386 & 67,576.6875 & 11,525.2718 \\ AGGL\({}_{3}\) & 43,867.1211 & 68,638.1953 & 58,670.0921 & 67,070.9375 & 11,658.5487 \\ AGGL\({}_{5}\) & 44,337.6641 & 68,679.3125 & 61,675.6590 & 67,577.0625 & 10,517.0718 \\ AGGL\({}_{7}\) & 43,688.3750 & 689,976.5469 & 64,480.6272 & 67,636.1788 & 912.1896 \\ AGGL\({}_{10}\) & 66,566.2969 & 68,004.2500 & 67,175.4855 & 67,188.0938 & 510.9257 \\ AGGL\({}_{15}\) & 43,160.3125 & 67,295.1797 & 57,406.8198 & 66,625.8047 & 12,069.9982 \\ AGGL\({}_{20}\) & 43,819.1289 & 67,265.2188 & 60,462.2919 & 66,883.5938 & 11,234.3160 \\ AGGL\({}_{25}\) & 43,324.3203 & 67,231.0859 & 60,237.4286 & 66,842.1719 & 11,452.1535 \\ AGGL\({}_{50}\) & 42,628.9141 & 66,279.0781 & 62,340.2411 & 65,467.8828 & 8700.5474 \\ AGGL\({}_{100}\) & 64,635.0655 & 65,408.7891 & 65,055.4102 & 65,155.6328 & 284.9207 \\ AGGL\({}_{150}\) & 64,458.3047 & 65,008.1641 & 64,682.8622 & 64,706.7969 & 188.7753 \\ AGGL\({}_{200}\) & 41,754.1680 & 64,694.6328 & 85,240.8147 & 64,582.625 & 10,883.6398 \\ AGGL\({}_{250}\) & 64,385.2461 & 64,646.9141 & 64,538.0787 & 64,547.0820 & 82.7326 \\ AGGL\({}_{300}\) & 41,373.4453 & 65,051.3594 & 61,283.1027 & 64,492.8828 & 8781.8445 \\ GH-VNS1 & 67,290.1406 & 68,655.8125 & 67,645.6563 & 67,467.4375 & 478.1909 \\ GH-VNS2 & 43,395.0273 & 67,046.0234 & 63,081.6233 & 66,222.0313 & 8689.9374 \\ GH-VNS3 & 42,124.9375 & 64,425.2734 & 61,204.4487 & 64,358.4844 & 8413.3478 \\ SWAP\({}_{3}\) (the best of SWAP) & 46,137.9570 & 70,000.2813 & 63,618.6362 & 68,361.8672 & 9467.2699 \\ SWAP\({}_{100}\) (the best of SWAP) & 51,026.5859 & 70,514.3359 & 65,787.6618 & 67,617.5781 & 6667.2423 \\ GA-1 POINT & 45,618.3828 & 52,858.6875 & 49,198.6964 & 47,304.4453 & 3129.2994 \\ GA-UNIFORM & 46,171.7148 & 62,034.5273 & 52,218.1077 & 50,642.4961 & 5712.0544 \\ Aggl-EA & 41,795.5078 & 65,057.9375 & 51,888.0642 & 42,525.5000 & 12,122.2332 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparative results for Individual Household Electric Power Consumption (IHEPC) dataset. 2,075,259 data vectors in \(\mathbb{R}^{7}\), \(p\) = 30 centers, time limitation 5 min. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm or**} & \multicolumn{4}{c}{**Achieved Objective Function Values (1)**} & \multicolumn{1}{c}{**Summarized after 30 Runs**} \\ \cline{2-6}  & **Min (Record)** & **Max (Worst)** & **Average** & **Median** & **Std. Dev** \\ \hline Lloyd (multistart) & 88,145.6484 & 93,677.3281 & 90,681.1663 & 89,967.9297 & 1818.1249 \\ j-means (SWAP\({}_{1}\) + Lloyd) & 87,907.7813 & 95,055.2422 & 89,657.8895 & 88,702.9297 & 2442.1302 \\ AGGL\({}_{1}\) & 91,021.6016 & 110,467.5625 & 104,694.9900 & 109,976.7266 & 9278.2649 \\ AGGL\({}_{2}\) & 86,291.1406 & 109,972.0781 & 99,788.2522 & 109,817.8125 & 12,557.4381 \\ AGGL\({}_{3}\) & 109,817.8125 & 109,999.1328 & 109,913.6953 & 109,972.0781 & 90.3626 \\ AGGL\({}_{5}\) & 86,240.7344 & 109,999.1328 & 103,145.4487 & 109,817.8281 & 11,519.4208 \\ AGGL\({}_{7}\) & 86,345.9297 & 109,817.83