## 2 Methods
### CUDA Implementation of Greedy Agglomerative Procedures
100,000 data vectors in \(\mathbb{R}^{2}\), k = 30 clusters, time limitation 10 s & & \\ GH-VNS1 & \(3.45057\times 10^{9}\) & \(3.45057\times 10^{9}\) & \(3.45057\times 10^{9}\) & \(3.45057\times 10^{9}\) & 0.00000 \\ Aggl-EA\(\leftrightarrow\)\(\Leftrightarrow\) & \(3.45057\times 10^{9}\) & \(3.45057\times 10^{9}\) & \(3.45057\times 10^{9}\) & \(3.45057\times 10^{9}\) & 0.00000 \\  & BIRCH3 dataset. 100,000 data vectors in \(\mathbb{R}^{2}\), k = 100 clusters, time limitation 10 s & & \\ AGGL\({}_{2}\) & \(1.49271\times 10^{9}\) & \(1.57474\times 10^{9}\) & \(1.49822\times 10^{9}\) & \(1.49445\times 10^{9}\) & \(1.51721\times 10^{7}\) \\ AGGL\({}_{5}\) & \(1.49341\times 10^{9}\) & \(1.57503\times 10^{9}\) & \(1.50433\times 10^{9}\) & \(1.49439\times 10^{9}\) & \(2.44200\times 10^{7}\) \\ Aggl-EA\(\leftrightarrow\)\(\Leftrightarrow\) & \(1.49199\times 10^{9}\) & \(1.57449\times 10^{9}\) & \(1.50670\times 10^{9}\) & \(1.49495\times 10^{9}\) & \(2.46374\times 10^{7}\) \\  & BIRCH3 dataset. 100,000 data vectors in \(\mathbb{R}^{2}\), k = 300 clusters, time limitation 10 s & & \\ AGGL\({}_{250}\) & \(9.08532\times 10^{8}\) & \(9.78792\times 10^{8}\) & \(9.36947\times 10^{8}\) & \(9.29497\times 10^{8}\) & \(1.97893\times 10^{7}\) \\ GH-VNS3 & \(9.12455\times 10^{8}\) & \(9.44414\times 10^{8}\) & \(9.31225\times 10^{8}\) & \(9.32001\times 10^{8}\) & \(8.25653\times 10^{6}\) \\ Aggl-EA\(\leftrightarrow\)\(\Leftrightarrow\) & \(9.14179\times 10^{8}\) & \(9.71905\times 10^{8}\) & \(9.34535\times 10^{8}\) & \(9.33403\times 10^{8}\) & \(1.51920\times 10^{7}\) \\  & Mopsi-Joensuu dataset. 6014 data vectors in \(\mathbb{R}^{2}\), k = 30 clusters, time limitation 5 s & & \\ AGGL\({}_{20}\) & \(145.7784\) & \(145.8113\) & \(145.7869\) & \(145.7847\) & 0.0084 \\ GH-VNS3 & \(145.7721\) & \(146.1273\) & \(145.7932\) & \(145.7761\) & 0.0664 \\ Aggl-EA\(\uparrow\)\(\dagger\) & \(145.7721\) & \(145.7752\) & \(145.7738\) & \(145.7739\) & 0.0008 \\  & Mopsi-Joensuu dataset. 6014 data vectors in \(\mathbb{R}^{2}\), k = 100 clusters, time limitation 5 s & & \\ AGGL\({}_{75}\) & \(43.6375\) & \(45.6856\) & \(44.1450\) & \(43.9860\) & 0.4683 \\ AGGL\({}_{100}\) & \(43.6054\) & \(44.9035\) & \(44.0472\) & \(43.9875\) & 0.3061 \\ Aggl-EA\(\uparrow\)\(\dagger\) & \(43.5826\) & \(44.4452\) & \(43.7486\) & \(43.7560\) & 0.1624 \\  & Mopsi-Joensuu dataset. 6014 data vectors in \(\mathbb{R}^{2}\), k = 300 clusters, time limitation 5 s & & \\ AGGL\({}_{250}\) & \(15.0975\) & \(16.8500\) & \(15.7103\) & \(15.6757\) & 0.3640 \\ Aggl-EA\(\leftrightarrow\)\(\Leftrightarrow\) & \(14.8354\) & \(19.4531\) & \(15.4576\) & \(15.3310\) & 0.8131 \\  & IHEPC dataset. 2,075,259 data vectors in \(\mathbb{R}^{7}\), k = 30 clusters, time limitation 5 min & & \\ j-means & \(87907.7813\) & \(95,055.2422\) & \(89.657.8895\) & \(88.702.9297\) & \(2442.1302\) \\ Aggl-EA\(\leftrightarrow\)\(\Leftrightarrow\) & \(86147.6953\) & \(10,9817.7969\) & \(91,515.1658\) & \(86,393.3750\) & 10,377.0956 \\  & HEPC dataset. 2,075,259 data vectors in \(\mathbb{R}^{7}\), k = 100 clusters, time limitation 5 min s & & \\ GA-IPOINT & \(62,192.1719\) & \(64,413.2578\) & \(63,051.7500\) & \(63,028.9922\) & \(733.3966\) \\ Aggl-EA\(\leftrightarrow\)\(\dagger\) & \(57,594.0703\) & \(72,909.5000\) & \(65,782.6094\) & \(58,506.6484\) & \(3208.6879\) \\  & IHEPC dataset.