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R Under development (unstable) (2023-10-19 r85356) -- "Unsuffered Consequences"
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> ### Clara with really LARGE data set --- CPU testing
> if(!cluster:::doExtras()) q("no")
> 
> library(cluster)
> 
> ## generate 15*N objects, divided into 2 clusters.
> N <- 10000
> suppressWarnings(RNGversion("3.5.0")) # << as long as we don't have R >= 3.6.0
> set.seed(521)
> x <- rbind(cbind(rnorm(7*N, 0,8), rnorm(7*N, 0,8)),
+            cbind(rnorm(8*N,50,8), rnorm(8*N,10,8)))
> .proctime00 <- proc.time()
> 
> for(nn in 1:3)
+     print(clara2 <- clara(x[sample(nrow(x)),], 2, sampsize = 128, samples= 500))
Call:    clara(x = x[sample(nrow(x)), ], k = 2, samples = 500, sampsize = 128) 
Medoids:
           [,1]       [,2]
[1,] 49.9788949  9.9439603
[2,]  0.1102125 -0.2841381
Objective function:  10.02584
Clustering vector:   int [1:150000] 1 1 2 2 2 2 2 1 1 2 2 2 1 1 1 2 1 1 ...
Cluster sizes:           79981 70019 
Best sample:
  [1]   1477   1701   4107   4356   4759   4821   5038   9625  10218  11557
 [11]  14191  14335  18496  18597  19282  21021  21428  21756  24235  24999
 [21]  29625  30005  30028  31534  34102  34793  35798  37583  37880  39309
 [31]  39464  40016  40606  40888  43390  43593  44463  45022  45495  46132
 [41]  46333  46541  48075  49398  51433  52254  53476  54893  55111  56022
 [51]  57404  58564  59643  61785  62494  63611  64067  64479  67136  69370
 [61]  70595  70796  71588  73394  74023  74108  74415  75653  77717  79862
 [71]  80162  82613  83030  84101  85264  85470  88326  89514  89949  90716
 [81]  91182  91968  94286  98923 100015 101164 101970 102180 102784 103274
 [91] 104222 106634 106824 107181 107760 108751 108900 111452 113052 113750
[101] 113798 114279 114412 114604 114817 115128 115737 116311 117165 117627
[111] 120797 121450 127247 127808 128644 128930 129490 131072 133363 133532
[121] 135986 137348 137616 138968 142001 142585 146915 148520

Available components:
 [1] "sample"     "medoids"    "i.med"      "clustering" "objective" 
 [6] "clusinfo"   "diss"       "call"       "silinfo"    "data"      
Call:    clara(x = x[sample(nrow(x)), ], k = 2, samples = 500, sampsize = 128) 
Medoids:
           [,1]       [,2]
[1,] 49.9175264 10.1438794
[2,]  0.1102125 -0.2841381
Objective function:  10.02635
Clustering vector:   int [1:150000] 1 1 2 2 2 2 2 2 2 1 2 1 2 1 2 1 1 1 ...
Cluster sizes:           79983 70017 
Best sample:
  [1]   2967   6883   7036   7750   8490   8725  10957  13147  13770  14908
 [11]  16871  17121  17878  18167  19217  19897  22653  23189  23951  24358
 [21]  24619  27164  27929  27945  29094  29171  30110  30499  31204  31749
 [31]  32152  35388  35690  38727  40513  41728  42209  42341  42394  44475
 [41]  46523  47393  47951  51005  52236  53664  54172  54573  55177  55184
 [51]  56360  56406  56894  57017  57420  58040  60334  60897  61293  62572
 [61]  63412  63753  63916  64715  65424  65545  67164  69931  70066  76449
 [71]  78324  78825  79406  79640  81940  82286  82689  85756  85959  86271
 [81]  86648  87031  87555  87749  88463  91256  91459  94112  96426  96527
 [91]  96969  97216  99685  99825 101853 105071 107554 108637 109463 109754
[101] 110111 116680 118058 118536 118667 120095 120557 123107 123425 124061
[111] 124379 125808 125879 127508 128557 129362 130243 134379 136494 141191
[121] 141369 141997 142408 143285 145427 148524 148726 149518

Available components:
 [1] "sample"     "medoids"    "i.med"      "clustering" "objective" 
 [6] "clusinfo"   "diss"       "call"       "silinfo"    "data"      
Call:    clara(x = x[sample(nrow(x)), ], k = 2, samples = 500, sampsize = 128) 
Medoids:
           [,1]       [,2]
[1,]  0.1392328 -0.1579963
[2,] 49.9788949  9.9439603
Objective function:  10.02523
Clustering vector:   int [1:150000] 1 2 2 2 1 1 2 2 2 2 2 2 1 1 2 2 1 1 ...
Cluster sizes:           70020 79980 
Best sample:
  [1]   1770   2921   3843   4223   4246   4992   5420   5752   6386   7256
 [11]   7815   8320   9012  12099  13527  13683  14235  15047  15107  17904
 [21]  18789  18890  19714  20350  20552  20760  22049  22294  22435  23617
 [31]  24578  26457  27473  27695  29112  29515  30240  32783  36713  37830
 [41]  38286  39091  39705  40899  43589  47612  48242  48327  48633  49872
 [51]  51936  53975  54081  54381  54783  56832  58656  59482  59688  62027
 [61]  62545  63733  64168  64934  68505  71881  73142  73591  75376  76188
 [71]  77003  77493  79649  80853  82970  83119  84243  85671  87271  87969
 [81]  88498  88823  89036  89956  90530  91224  91384  92260  95016  95197
 [91] 100308 101466 101535 102027 102862 103148 104929 105291 107751 108053
[101] 111306 111834 113187 116803 116900 117614 120823 121134 121894 125920
[111] 126726 128978 129257 133844 134437 135775 136279 137792 138410 138554
[121] 139772 142294 143500 144617 144974 145647 145887 146089

Available components:
 [1] "sample"     "medoids"    "i.med"      "clustering" "objective" 
 [6] "clusinfo"   "diss"       "call"       "silinfo"    "data"      
> cat('Time elapsed: ', proc.time() - .proctime00,'\n')
Time elapsed:  4.313 0.009 4.328 0 0 
> ## nb-mm, with cluster 1.6-4 :  85.2 (elapsed 91.7)
> 
> ###-- Larger example: 20*N objects, divided into 5 clusters.
> N <- 20000 # 100'000 would give swapping like crazy for 256 MB RAM
> x5 <- rbind(cbind(rnorm(4*N, 0,4), rnorm(4*N, 0,4)),
+             cbind(rnorm(4*N,10,8), rnorm(4*N,40,6)),
+             cbind(rnorm(4*N,30,4), rnorm(4*N, 0,4)),
+             cbind(rnorm(4*N,40,4), rnorm(4*N,20,2)),
+             cbind(rnorm(4*N,50,4), rnorm(4*N,50,4)))
> ## plus 1 random dimension
> x5 <- cbind(x5, rnorm(nrow(x5)))
> 
> .proctime00 <- proc.time()
> for(nn in 1:3)
+     print(clara(x5[sample(nrow(x5)),], 5, samples= 100))
Call:    clara(x = x5[sample(nrow(x5)), ], k = 5, samples = 100) 
Medoids:
           [,1]       [,2]       [,3]
[1,] 51.1002305 50.1460959  0.1331874
[2,]  9.1252025 39.6689092 -0.4555514
[3,] 29.5625766  1.1976994 -0.9082990
[4,] 39.8055168 20.2326953  0.5889404
[5,]  0.2465901  0.8764563 -0.6630726
Objective function:  5.769338
Clustering vector:   int [1:400000] 1 2 3 3 1 4 4 3 2 3 1 2 4 1 3 3 1 1 ...
Cluster sizes:           80240 79099 79851 80781 80029 
Best sample:
 [1]  13178  26722  33924  43964  45752  50483  67536  75464  80530 106348
[11] 108637 110810 127118 129499 142328 144721 149683 164087 224817 234113
[21] 237732 238459 242896 248731 260828 274946 275422 277540 285279 294501
[31] 307300 316791 316944 327656 328492 332435 334339 338190 346882 350599
[41] 353437 357020 358844 360273 364637 376227 378626 379767 388135 392804

Available components:
 [1] "sample"     "medoids"    "i.med"      "clustering" "objective" 
 [6] "clusinfo"   "diss"       "call"       "silinfo"    "data"      
Call:    clara(x = x5[sample(nrow(x5)), ], k = 5, samples = 100) 
Medoids:
           [,1]       [,2]       [,3]
[1,] 30.0804463 -0.9518318 -0.3667627
[2,] 40.4359809 19.4916731  0.5483652
[3,] -0.8077057  0.4248833  0.1078008
[4,] 10.5285200 41.9423158  0.7195955
[5,] 49.6862611 49.7783134  0.1508043
Objective function:  5.760039
Clustering vector:   int [1:400000] 1 2 2 3 1 2 1 1 1 3 3 3 2 1 4 2 5 2 ...
Cluster sizes:           79697 80784 80058 79206 80255 
Best sample:
 [1]  43488  47107  47834  50916  52271  70203  84321  84797  86915 116675
[11] 126166 126319 130451 137031 137867 141810 143714 147565 152802 156257
[21] 166395 168219 169648 174012 188001 189142 192475 197510 202179 214356
[31] 236097 243299 253339 255127 259858 275190 276911 282416 289905 294117
[41] 314289 315723 318012 320185 331080 336493 338874 351703 359058 373462

Available components:
 [1] "sample"     "medoids"    "i.med"      "clustering" "objective" 
 [6] "clusinfo"   "diss"       "call"       "silinfo"    "data"      
Call:    clara(x = x5[sample(nrow(x5)), ], k = 5, samples = 100) 
Medoids:
           [,1]        [,2]        [,3]
[1,] 10.6835651 40.49264498  0.37786959
[2,]  0.7370939 -0.09727728 -0.52975660
[3,] 30.9806583 -0.03202827 -0.12876557
[4,] 39.1749167 20.45583496 -0.08582161
[5,] 49.4757529 49.73885441  0.25604496
Objective function:  5.725015
Clustering vector:   int [1:400000] 1 2 3 2 4 5 2 2 5 4 4 3 5 3 2 2 1 2 ...
Cluster sizes:           79138 80053 79818 80737 80254 
Best sample:
 [1]   3217  11579  28962  51465  59754  61371  67371  80274  80390  86847
[11]  91193  95063 110010 113007 119678 120270 128156 136658 152417 166822
[21] 173810 182233 190870 196698 227680 232502 235169 237073 245539 245630
[31] 248487 253705 259870 263544 269526 277717 279853 281360 309083 310035
[41] 329456 331226 335834 340467 344275 346692 363563 383265 390925 399616

Available components:
 [1] "sample"     "medoids"    "i.med"      "clustering" "objective" 
 [6] "clusinfo"   "diss"       "call"       "silinfo"    "data"      
> ## Last Line:
> cat("Time elapsed: ", proc.time() - .proctime00,"\n")
Time elapsed:  4.214 0.022 4.242 0 0 
> ## nb-mm, with cluster 1.6-4 : 74.4 (elapsed 88.2)
> ## ~~~~~  1.7-* quite faster : 67.6 (elapsed 68.7)
> 
> proc.time()
   user  system elapsed 
  8.860   0.141   9.197