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R Under development (unstable) (2023-10-19 r85354) -- "Unsuffered Consequences"
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> library(cluster)
> 
> ## generate 1500 objects, divided into 2 clusters.
> suppressWarnings(RNGversion("3.5.0")) # << as long as we don't have R >= 3.6.0
> set.seed(144)
> x <- rbind(cbind(rnorm(700, 0,8), rnorm(700, 0,8)),
+            cbind(rnorm(800,50,8), rnorm(800,10,8)))
> 
> isEq <- function(x,y, epsF = 100)
+     is.logical(r <- all.equal(x,y, tol = epsF * .Machine$double.eps)) && r
> 
> .proctime00 <- proc.time()
> 
> ## full size sample {should be = pam()}:
> n0 <- length(iSml <- c(1:70, 701:720))
> summary(clara0 <- clara(x[iSml,], k = 2, sampsize = n0))
Object of class 'clara' from call:
 clara(x = x[iSml, ], k = 2, sampsize = n0) 
Medoids:
          [,1]      [,2]
[1,] -1.499522 -1.944452
[2,] 48.629631 12.998515
Objective function:   10.23588 
Numerical information per cluster:
     size max_diss  av_diss isolation
[1,]   70 24.81995 10.25745 0.4744879
[2,]   20 19.07782 10.16040 0.3647145
Average silhouette width per cluster:
[1] 0.7144587 0.7090915
Average silhouette width of best sample: 0.713266 

Best sample:
 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
[26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
[51] 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75
[76] 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
Clustering vector:
 [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[39] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2
[77] 2 2 2 2 2 2 2 2 2 2 2 2 2 2

Silhouette plot information for best sample:
   cluster neighbor sil_width
45       1        2 0.8033727
60       1        2 0.8021017
55       1        2 0.8005931
66       1        2 0.8002776
58       1        2 0.7991899
11       1        2 0.7991773
41       1        2 0.7973302
26       1        2 0.7962397
63       1        2 0.7962229
13       1        2 0.7949705
67       1        2 0.7942590
54       1        2 0.7936184
17       1        2 0.7916087
16       1        2 0.7913570
39       1        2 0.7912755
6        1        2 0.7840455
34       1        2 0.7833568
49       1        2 0.7819733
9        1        2 0.7789087
23       1        2 0.7785009
32       1        2 0.7757325
22       1        2 0.7655369
61       1        2 0.7639754
12       1        2 0.7639644
5        1        2 0.7606436
18       1        2 0.7579145
56       1        2 0.7566307
3        1        2 0.7537894
24       1        2 0.7531180
50       1        2 0.7517817
48       1        2 0.7501998
25       1        2 0.7499655
59       1        2 0.7472022
19       1        2 0.7445038
65       1        2 0.7398395
28       1        2 0.7377377
38       1        2 0.7370935
7        1        2 0.7335940
40       1        2 0.7310012
14       1        2 0.7294895
62       1        2 0.7254478
70       1        2 0.7163214
4        1        2 0.7157257
21       1        2 0.7148663
64       1        2 0.7108496
2        1        2 0.7062831
15       1        2 0.7015120
52       1        2 0.6978313
37       1        2 0.6954023
31       1        2 0.6932905
33       1        2 0.6888478
10       1        2 0.6805028
20       1        2 0.6766854
43       1        2 0.6761461
8        1        2 0.6749706
27       1        2 0.6671817
35       1        2 0.6632888
68       1        2 0.6587599
30       1        2 0.6554989
36       1        2 0.6228481
53       1        2 0.6203313
57       1        2 0.6191666
42       1        2 0.6142020
47       1        2 0.6024151
1        1        2 0.5814464
69       1        2 0.5091186
46       1        2 0.4961302
44       1        2 0.4849961
29       1        2 0.4569316
51       1        2 0.4230181
81       2        1 0.7965942
71       2        1 0.7961971
85       2        1 0.7919593
74       2        1 0.7869047
82       2        1 0.7795304
78       2        1 0.7788873
79       2        1 0.7729041
72       2        1 0.7492980
88       2        1 0.7447973
87       2        1 0.7404399
76       2        1 0.7352351
77       2        1 0.7216838
86       2        1 0.7165677
84       2        1 0.6952406
73       2        1 0.6942882
83       2        1 0.6621568
80       2        1 0.6368446
90       2        1 0.5743228
75       2        1 0.5597232
89       2        1 0.4482549

4005 dissimilarities, summarized :
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.1865 11.5850 20.0580 27.8150 45.5780 85.2320 
Metric :  euclidean 
Number of objects : 90

Available components:
 [1] "sample"     "medoids"    "i.med"      "clustering" "objective" 
 [6] "clusinfo"   "diss"       "call"       "silinfo"    "data"      
>           pam0 <- pam  (x[iSml,], k = 2)
> stopifnot(identical(clara0$clustering, pam0$clustering)
+         , isEq(clara0$objective, unname(pam0$objective[2]))
+           )
> 
> summary(clara2 <- clara(x, 2))
Object of class 'clara' from call:
 clara(x = x, k = 2) 
Medoids:
          [,1]      [,2]
[1,]  2.012828 -1.896095
[2,] 51.494628 10.274769
Objective function:   10.23445 
Numerical information per cluster:
     size max_diss  av_diss isolation
[1,]  700 36.84408 10.49814 0.7230478
[2,]  800 30.89896 10.00373 0.6063775
Average silhouette width per cluster:
[1] 0.7562366 0.7203254
Average silhouette width of best sample: 0.733384 

Best sample:
 [1]   21   23   50   97  142  168  191  192  197  224  325  328  433  458  471
[16]  651  712  714  722  797  805  837  909  919  926  999 1006 1018 1019 1049
[31] 1081 1084 1132 1144 1150 1201 1207 1250 1291 1307 1330 1374 1426 1428
Clustering vector:
   [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [667] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2
 [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [815] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [852] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [889] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [926] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [963] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[1000] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[1037] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[1074] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[1111] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[1148] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[1185] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[1222] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[1259] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[1296] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[1333] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[1370] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[1407] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[1444] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[1481] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

Silhouette plot information for best sample:
     cluster neighbor sil_width
325        1        2 0.8261589
191        1        2 0.8206687
23         1        2 0.8149640
97         1        2 0.8048084
433        1        2 0.8017745
458        1        2 0.8008324
471        1        2 0.7958547
328        1        2 0.7689099
142        1        2 0.7619508
21         1        2 0.7607528
197        1        2 0.7606641
50         1        2 0.7509131
192        1        2 0.7098473
651        1        2 0.7035969
224        1        2 0.6843886
168        1        2 0.5337006
1084       2        1 0.8180447
1081       2        1 0.8171686
1201       2        1 0.8170847
1291       2        1 0.8167148
1307       2        1 0.8166841
1144       2        1 0.8159947
999        2        1 0.8135303
1426       2        1 0.8023538
1049       2        1 0.8022891
1250       2        1 0.8014300
712        2        1 0.7859324
837        2        1 0.7792784
1018       2        1 0.7764837
919        2        1 0.7651939
1374       2        1 0.7648534
1428       2        1 0.7516819
1330       2        1 0.7505861
1006       2        1 0.7368113
714        2        1 0.7237565
1150       2        1 0.7046060
1132       2        1 0.6940608
909        2        1 0.6859682
926        2        1 0.6725631
722        2        1 0.6572791
797        2        1 0.6395698
1019       2        1 0.6083662
805        2        1 0.2814164
1207       2        1 0.2694097

946 dissimilarities, summarized :
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.4846 12.3230 26.4990 32.2130 52.3910 77.1750 
Metric :  euclidean 
Number of objects : 44

Available components:
 [1] "sample"     "medoids"    "i.med"      "clustering" "objective" 
 [6] "clusinfo"   "diss"       "call"       "silinfo"    "data"      
> 
> clInd <- c("objective", "i.med", "medoids", "clusinfo")
> clInS <- c(clInd, "sample")
> ## clara() {as original code} always draws the *same* random samples !!!!
> clara(x, 2, samples = 50)[clInd]
$objective
[1] 10.06735

$i.med
[1]  177 1115

$medoids
           [,1]      [,2]
[1,] -0.2538744 -1.209148
[2,] 50.0372683  9.501125

$clusinfo
     size max_diss   av_diss isolation
[1,]  700 34.67208 10.193945 0.6743054
[2,]  800 29.51964  9.956571 0.5741003

> for(i in 1:20)
+     print(clara(x[sample(nrow(x)),], 2, samples = 50)[clInd])
$objective
[1] 10.05727

$i.med
[1] 936 192

$medoids
            [,1]         [,2]
[1,] 50.03726827  9.501124850
[2,] -0.03900399 -0.009078886

$clusinfo
     size max_diss   av_diss isolation
[1,]  800 29.51964  9.956571 0.5791419
[2,]  700 34.06055 10.172348 0.6682295

$objective
[1] 10.05296

$i.med
[1]  468 1394

$medoids
           [,1]       [,2]
[1,] -0.3292826 -0.2398794
[2,] 50.0372683  9.5011249

$clusinfo
     size max_diss   av_diss isolation
[1,]  700 33.98451 10.163128 0.6624677
[2,]  800 29.51964  9.956571 0.5754330

$objective
[1] 10.05852

$i.med
[1] 1171  379

$medoids
           [,1]       [,2]
[1,] 50.9444060  9.6723175
[2,] -0.3292826 -0.2398794

$clusinfo
     size max_diss   av_diss isolation
[1,]  800 30.10388  9.966988 0.5764486
[2,]  700 33.98451 10.163128 0.6507574

$objective
[1] 10.07051

$i.med
[1]   75 1254

$medoids
           [,1]      [,2]
[1,] -0.9493373 0.3552542
[2,] 50.5455985 9.3904972

$clusinfo
     size max_diss   av_diss isolation
[1,]  700 33.12704 10.191999 0.6336273
[2,]  800 29.66384  9.964205 0.5673860

$objective
[1] 10.0613

$i.med
[1] 199 134

$medoids
            [,1]         [,2]
[1,] -0.03900399 -0.009078886
[2,] 49.59384120  9.792964832

$clusinfo
     size max_diss   av_diss isolation
[1,]  700 34.06055 10.172348 0.6732466
[2,]  800 29.57491  9.964138 0.5845827

$objective
[1] 10.06101

$i.med
[1] 1453 1122

$medoids
           [,1]       [,2]
[1,] 50.0372683 9.50112485
[2,] -0.9691441 0.03342515

$clusinfo
     size max_diss   av_diss isolation
[1,]  800 29.51964  9.956571 0.5690241
[2,]  700 33.31923 10.180359 0.6422655

$objective
[1] 10.08603

$i.med
[1] 613 318

$medoids
           [,1]     [,2]
[1,] 50.0627056 9.478225
[2,] -0.2902194 1.026496

$clusinfo
     size max_diss   av_diss isolation
[1,]  800 29.51131  9.957225 0.5780037
[2,]  700 33.21560 10.233240 0.6505552

$objective
[1] 10.07293

$i.med
[1] 618 406

$medoids
           [,1]       [,2]
[1,] 50.3621263  9.0207185
[2,] -0.2092816 -0.5916053

$clusinfo
     size max_diss   av_diss isolation
[1,]  800 29.25143  9.990206 0.5682446
[2,]  700 34.30301 10.167473 0.6663777

$objective
[1] 10.0592

$i.med
[1] 1279 1349

$medoids
           [,1]        [,2]
[1,] 50.1502433 10.60358224
[2,] -0.9691441  0.03342515

$clusinfo
     size max_diss   av_diss isolation
[1,]  800 30.54975  9.953191 0.5852356
[2,]  700 33.31923 10.180359 0.6382900

$objective
[1] 10.06241

$i.med
[1] 1293   21

$medoids
           [,1]      [,2]
[1,] 50.5809098 9.7418386
[2,] -0.9493373 0.3552542

$clusinfo
     size max_diss   av_diss isolation
[1,]  800 29.98892  9.949013 0.5725461
[2,]  700 33.12704 10.191999 0.6324587

$objective
[1] 10.0592

$i.med
[1] 337 675

$medoids
           [,1]        [,2]
[1,] -0.9691441  0.03342515
[2,] 50.1502433 10.60358224

$clusinfo
     size max_diss   av_diss isolation
[1,]  700 33.31923 10.180359 0.6382900
[2,]  800 30.54975  9.953191 0.5852356

$objective
[1] 10.05697

$i.med
[1]  22 574

$medoids
           [,1]       [,2]
[1,] 50.5809098 9.74183863
[2,] -0.9691441 0.03342515

$clusinfo
     size max_diss   av_diss isolation
[1,]  800 29.98892  9.949013 0.5716937
[2,]  700 33.31923 10.180359 0.6351809

$objective
[1] 10.05096

$i.med
[1] 739 808

$medoids
           [,1]       [,2]
[1,] 50.5809098  9.7418386
[2,] -0.2092816 -0.5916053

$clusinfo
     size max_diss   av_diss isolation
[1,]  800 29.98892  9.949013 0.5785936
[2,]  700 34.30301 10.167473 0.6618278

$objective
[1] 10.06135

$i.med
[1] 1431  485

$medoids
           [,1]       [,2]
[1,] 50.0627056 9.47822525
[2,] -0.9691441 0.03342515

$clusinfo
     size max_diss   av_diss isolation
[1,]  800 29.51131  9.957225 0.5686352
[2,]  700 33.31923 10.180359 0.6420076

$objective
[1] 10.05324

$i.med
[1]   10 1221

$medoids
            [,1]         [,2]
[1,] 50.58090982  9.741838628
[2,] -0.03900399 -0.009078886

$clusinfo
     size max_diss   av_diss isolation
[1,]  800 29.98892  9.949013 0.5817385
[2,]  700 34.06055 10.172348 0.6607218

$objective
[1] 10.06101

$i.med
[1] 1249 1411

$medoids
           [,1]       [,2]
[1,] -0.9691441 0.03342515
[2,] 50.0372683 9.50112485

$clusinfo
     size max_diss   av_diss isolation
[1,]  700 33.31923 10.180359 0.6422655
[2,]  800 29.51964  9.956571 0.5690241

$objective
[1] 10.05296

$i.med
[1] 610  21

$medoids
           [,1]       [,2]
[1,] -0.3292826 -0.2398794
[2,] 50.0372683  9.5011249

$clusinfo
     size max_diss   av_diss isolation
[1,]  700 33.98451 10.163128 0.6624677
[2,]  800 29.51964  9.956571 0.5754330

$objective
[1] 10.06486

$i.med
[1] 1101  397

$medoids
           [,1]       [,2]
[1,] -0.9691441 0.03342515
[2,] 50.1066826 9.35514422

$clusinfo
     size max_diss   av_diss isolation
[1,]  700 33.31923 10.180359 0.6417479
[2,]  800 29.42336  9.963794 0.5667111

$objective
[1] 10.07521

$i.med
[1] 838 356

$medoids
            [,1]         [,2]
[1,] 50.36212634  9.020718482
[2,] -0.03900399 -0.009078886

$clusinfo
     size max_diss   av_diss isolation
[1,]  800 29.25143  9.990206 0.5712766
[2,]  700 34.06055 10.172348 0.6651980

$objective
[1] 10.05906

$i.med
[1] 1270 1024

$medoids
           [,1]       [,2]
[1,] 50.5455985  9.3904972
[2,] -0.2092816 -0.5916053

$clusinfo
     size max_diss   av_diss isolation
[1,]  800 29.66384  9.964205 0.5734673
[2,]  700 34.30301 10.167473 0.6631526

> 
> clara(x, 2, samples = 101)[clInd]
$objective
[1] 10.05727

$i.med
[1]  286 1115

$medoids
            [,1]         [,2]
[1,] -0.03900399 -0.009078886
[2,] 50.03726827  9.501124850

$clusinfo
     size max_diss   av_diss isolation
[1,]  700 34.06055 10.172348 0.6682295
[2,]  800 29.51964  9.956571 0.5791419

> clara(x, 2, samples = 149)[clInd]
$objective
[1] 10.05319

$i.med
[1]  238 1272

$medoids
           [,1]       [,2]
[1,] -0.2092816 -0.5916053
[2,] 50.1502433 10.6035822

$clusinfo
     size max_diss   av_diss isolation
[1,]  700 34.30301 10.167473 0.6649301
[2,]  800 30.54975  9.953191 0.5921768

> clara(x, 2, samples = 200)[clInd]
$objective
[1] 10.05319

$i.med
[1]  238 1272

$medoids
           [,1]       [,2]
[1,] -0.2092816 -0.5916053
[2,] 50.1502433 10.6035822

$clusinfo
     size max_diss   av_diss isolation
[1,]  700 34.30301 10.167473 0.6649301
[2,]  800 30.54975  9.953191 0.5921768

> ## Note that this last one is practically identical to the slower  pam() one
> 
> (ii <- sample(length(x), 20))
 [1]  249  452 2663 2537 2235 2421 1004 1834 2602  397  717 2805 1575 1281  283
[16] 1657 1749  820  269  519
> ## This was bogous (and lead to seg.faults); now properly gives error.
> ## but for these, now see  ./clara-NAs.R
> if(FALSE) { ##           ~~~~~~~~~~~~~
+     x[ii] <- NA
+     try( clara(x, 2, samples = 50) )
+ }
> 
> ###-- Larger example: 2000 objects, divided into 5 clusters.
> x5 <- rbind(cbind(rnorm(400, 0,4), rnorm(400, 0,4)),
+             cbind(rnorm(400,10,8), rnorm(400,40,6)),
+             cbind(rnorm(400,30,4), rnorm(400, 0,4)),
+             cbind(rnorm(400,40,4), rnorm(400,20,2)),
+             cbind(rnorm(400,50,4), rnorm(400,50,4)))
> ## plus 1 random dimension
> x5 <- cbind(x5, rnorm(nrow(x5)))
> 
> clara(x5, 5)
Call:    clara(x = x5, k = 5) 
Medoids:
           [,1]      [,2]        [,3]
[1,]  0.5850466 -2.222194 -0.63631241
[2,]  8.0131143 42.708122 -0.31693240
[3,] 42.6657812 21.123133 -0.62411426
[4,] 50.6470292 48.480686 -0.09146223
[5,] 28.6470950 -2.544131 -0.22186047
Objective function:  6.100721
Clustering vector:   int [1:2000] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ...
Cluster sizes:           400 396 408 401 395 
Best sample:
 [1]   23  130  178  202  267  297  338  357  376  387  439  441  638  647  662
[16]  719  723  802  874  880  994 1038 1056 1097 1184 1215 1225 1268 1271 1282
[31] 1346 1442 1446 1474 1496 1515 1585 1590 1605 1641 1680 1687 1696 1728 1742
[46] 1761 1857 1909 1951 1956

Available components:
 [1] "sample"     "medoids"    "i.med"      "clustering" "objective" 
 [6] "clusinfo"   "diss"       "call"       "silinfo"    "data"      
> summary(clara(x5, 5, samples = 50))
Object of class 'clara' from call:
 clara(x = x5, k = 5, samples = 50) 
Medoids:
           [,1]       [,2]        [,3]
[1,] -0.8427864  0.1606105 -0.70362181
[2,] 12.0389703 39.0303445  0.19158023
[3,] 39.6341676 20.7182868  0.43978514
[4,] 50.6470292 48.4806864 -0.09146223
[5,] 30.6814242 -0.1072177 -0.25861548
Objective function:   5.743812 
Numerical information per cluster:
     size max_diss  av_diss isolation
[1,]  400 15.20728 5.207177 0.4823345
[2,]  397 24.25898 8.677062 0.7324727
[3,]  406 18.39064 4.369617 0.8109074
[4,]  401 18.28050 5.260543 0.6119680
[5,]  396 12.69653 5.243478 0.5598344
Average silhouette width per cluster:
[1] 0.7433532 0.6956424 0.7315944 0.7336104 0.7079360
Average silhouette width of best sample: 0.7188531 

Best sample:
 [1]  106  130  145  213  275  316  434  444  486  501  630  693  713  739  773
[16]  804  808  821  823  899  914  948  961  972  980  987 1076 1114 1126 1127
[31] 1169 1175 1203 1225 1228 1242 1269 1397 1405 1421 1595 1606 1658 1703 1777
[46] 1834 1857 1881 1937 1999
Clustering vector:
   [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2
 [408] 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [593] 2 2 2 2 3 2 2 2 2 2 2 2 2 2 2 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5
 [815] 5 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
 [852] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
 [889] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
 [926] 5 5 5 5 5 5 5 5 5 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
 [963] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[1000] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[1037] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[1074] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[1111] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[1148] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[1185] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1222] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1259] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1296] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1333] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1370] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1407] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1444] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1481] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1518] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1555] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[1592] 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[1629] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[1666] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[1703] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[1740] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[1777] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[1814] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[1851] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[1888] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[1925] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[1962] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[1999] 4 4

Silhouette plot information for best sample:
     cluster neighbor sil_width
130        1        5 0.8123353
275        1        5 0.7945197
316        1        5 0.7561799
213        1        5 0.7459412
106        1        5 0.6869957
145        1        5 0.6641473
630        2        3 0.7819320
739        2        3 0.7774128
486        2        3 0.7559683
713        2        3 0.7316982
444        2        3 0.7204625
501        2        3 0.7091146
773        2        1 0.6886472
693        2        3 0.5855803
434        2        3 0.5099654
1225       3        5 0.8105776
1203       3        5 0.7965773
1595       3        5 0.7842711
1269       3        5 0.7799931
1242       3        5 0.7625442
1397       3        5 0.7315512
1228       3        5 0.7262025
1421       3        5 0.6011616
1405       3        5 0.5914707
1999       4        3 0.8050046
1857       4        3 0.8030709
1658       4        3 0.7941141
1777       4        3 0.7865209
1937       4        3 0.7831996
1881       4        3 0.7504779
1834       4        3 0.6614223
1606       4        3 0.6373808
1703       4        3 0.5813025
804        5        3 0.8021043
987        5        3 0.7999064
1076       5        3 0.7907769
948        5        3 0.7905304
961        5        3 0.7716289
823        5        3 0.7657693
808        5        3 0.7510670
914        5        3 0.7358231
1175       5        3 0.7337485
1169       5        3 0.7254812
972        5        3 0.7118795
821        5        3 0.7101558
899        5        1 0.6580927
1114       5        3 0.6552887
1127       5        3 0.6292428
1126       5        3 0.5362475
980        5        1 0.4671695

1225 dissimilarities, summarized :
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.6968 19.3160 34.0920 33.0700 46.2540 92.2530 
Metric :  euclidean 
Number of objects : 50

Available components:
 [1] "sample"     "medoids"    "i.med"      "clustering" "objective" 
 [6] "clusinfo"   "diss"       "call"       "silinfo"    "data"      
> ## 3 "half" samples:
> clara(x5, 5, samples = 999)
Call:    clara(x = x5, k = 5, samples = 999) 
Medoids:
           [,1]       [,2]        [,3]
[1,]  0.2143499  0.3891695  0.45577894
[2,] 10.9779485 39.6788652 -0.23487762
[3,] 40.2944064 20.2221253  0.21417849
[4,] 50.7170411 49.7645642 -0.43318939
[5,] 29.7257398 -0.5981739 -0.05616701
Objective function:  5.659041
Clustering vector:   int [1:2000] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ...
Cluster sizes:           400 397 407 401 395 
Best sample:
 [1]    1    2  103  147  155  176  179  247  262  288  365  369  372  470  486
[16]  573  759  779  785  791  797  822  875  883  913  954 1107 1114 1154 1156
[31] 1171 1175 1206 1213 1218 1233 1243 1394 1439 1444 1512 1741 1777 1798 1800
[46] 1818 1845 1946 1948 1973

Available components:
 [1] "sample"     "medoids"    "i.med"      "clustering" "objective" 
 [6] "clusinfo"   "diss"       "call"       "silinfo"    "data"      
> clara(x5, 5, samples = 1000)
Call:    clara(x = x5, k = 5, samples = 1000) 
Medoids:
           [,1]       [,2]        [,3]
[1,]  0.2143499  0.3891695  0.45577894
[2,] 10.9779485 39.6788652 -0.23487762
[3,] 40.2944064 20.2221253  0.21417849
[4,] 50.7170411 49.7645642 -0.43318939
[5,] 29.7257398 -0.5981739 -0.05616701
Objective function:  5.659041
Clustering vector:   int [1:2000] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ...
Cluster sizes:           400 397 407 401 395 
Best sample:
 [1]    1    2  103  147  155  176  179  247  262  288  365  369  372  470  486
[16]  573  759  779  785  791  797  822  875  883  913  954 1107 1114 1154 1156
[31] 1171 1175 1206 1213 1218 1233 1243 1394 1439 1444 1512 1741 1777 1798 1800
[46] 1818 1845 1946 1948 1973

Available components:
 [1] "sample"     "medoids"    "i.med"      "clustering" "objective" 
 [6] "clusinfo"   "diss"       "call"       "silinfo"    "data"      
> clara(x5, 5, samples = 1001)
Call:    clara(x = x5, k = 5, samples = 1001) 
Medoids:
           [,1]       [,2]        [,3]
[1,]  0.2143499  0.3891695  0.45577894
[2,] 10.9779485 39.6788652 -0.23487762
[3,] 40.2944064 20.2221253  0.21417849
[4,] 50.7170411 49.7645642 -0.43318939
[5,] 29.7257398 -0.5981739 -0.05616701
Objective function:  5.659041
Clustering vector:   int [1:2000] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ...
Cluster sizes:           400 397 407 401 395 
Best sample:
 [1]    1    2  103  147  155  176  179  247  262  288  365  369  372  470  486
[16]  573  759  779  785  791  797  822  875  883  913  954 1107 1114 1154 1156
[31] 1171 1175 1206 1213 1218 1233 1243 1394 1439 1444 1512 1741 1777 1798 1800
[46] 1818 1845 1946 1948 1973

Available components:
 [1] "sample"     "medoids"    "i.med"      "clustering" "objective" 
 [6] "clusinfo"   "diss"       "call"       "silinfo"    "data"      
> 
> clara(x5, 5, samples = 2000)#full sample
Call:    clara(x = x5, k = 5, samples = 2000) 
Medoids:
           [,1]       [,2]        [,3]
[1,]  0.2143499  0.3891695  0.45577894
[2,] 10.5993345 39.8970536 -0.39199265
[3,] 40.3370139 20.3148331 -0.06033818
[4,] 50.7170411 49.7645642 -0.43318939
[5,] 29.7257398 -0.5981739 -0.05616701
Objective function:  5.65785
Clustering vector:   int [1:2000] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ...
Cluster sizes:           400 397 407 401 395 
Best sample:
 [1]   84  106  164  226  284  288  329  423  430  450  469  593  603  654  742
[16]  887  929  970  974 1035 1043 1096 1171 1187 1192 1302 1307 1327 1371 1431
[31] 1433 1439 1440 1452 1513 1522 1525 1548 1565 1593 1620 1639 1654 1688 1740
[46] 1761 1832 1845 1895 1899

Available components:
 [1] "sample"     "medoids"    "i.med"      "clustering" "objective" 
 [6] "clusinfo"   "diss"       "call"       "silinfo"    "data"      
> 
> ###--- Start a version of  example(clara) -------
> 
> ## xclara : artificial data with 3 clusters of 1000 bivariate objects each.
> data(xclara)
> (clx3 <- clara(xclara, 3))
Call:    clara(x = xclara, k = 3) 
Medoids:
            V1        V2
[1,]  5.553391 13.306260
[2,] 43.198760 60.360720
[3,] 74.591890 -6.969018
Objective function:  13.225
Clustering vector:   int [1:3000] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ...
Cluster sizes:           900 1148 952 
Best sample:
 [1]   20   30   46   91   92  169  179  187  209  223  382  450  555  971 1004
[16] 1025 1058 1277 1281 1302 1319 1361 1362 1513 1591 1623 1628 1729 1752 1791
[31] 1907 1917 1946 2064 2089 2498 2527 2537 2545 2591 2672 2722 2729 2790 2797
[46] 2852

Available components:
 [1] "sample"     "medoids"    "i.med"      "clustering" "objective" 
 [6] "clusinfo"   "diss"       "call"       "silinfo"    "data"      
> ## Plot similar to Figure 5 in Struyf et al (1996)
> plot(clx3)
> 
> ## The  rngR = TRUE case is currently in the non-strict tests
> ## ./clara-ex.R
> ## ~~~~~~~~~~~~
> 
> ###--- End version of example(clara) -------
> 
> ##  small example(s):
> data(ruspini)
> 
> clara(ruspini,4)
Call:    clara(x = ruspini, k = 4) 
Medoids:
    x   y
10 19  65
32 44 149
52 99 119
67 66  18
Objective function:  11.51066
Clustering vector:   Named int [1:75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ...
 - attr(*, "names")= chr [1:75] "1" "2" "3" "4" "5" "6" "7" ...
Cluster sizes:           20 23 17 15 
Best sample:
 [1] 2  3  4  5  6  7  8  9  10 16 18 19 20 21 22 23 25 29 30 32 34 35 36 37 41
[26] 42 43 44 46 47 49 50 52 53 54 58 59 60 61 63 65 66 67 69 71 72 73 75

Available components:
 [1] "sample"     "medoids"    "i.med"      "clustering" "objective" 
 [6] "clusinfo"   "diss"       "call"       "silinfo"    "data"      
> 
> rus <- data.matrix(ruspini); storage.mode(rus) <- "double"
> ru2 <- rus[c(1:7,21:28, 45:51, 61:69),]
> ru3 <- rus[c(1:4,21:25, 45:48, 61:63),]
> ru4 <- rus[c(1:2,21:22, 45:47),]
> ru5 <- rus[c(1:2,21,    45),]
> daisy(ru5, "manhattan")
Dissimilarities :
     1   2  21
2   11        
21 118 107    
45 143 132  89

Metric :  manhattan 
Number of objects : 4
> ## Dissimilarities :  11 118 143 107 132  89
> 
> ## no problem anymore, since 2002-12-28:
> ## sampsize >= k+1 is now enforced:
> ## clara(ru5, k=3, met="manhattan", sampsize=3,trace=2)[clInS]
> clara(ru5, k=3, met="manhattan", sampsize=4,trace=1)[clInS]
C clara(): (nsam,nran,n) = (4,5,4); 'full_sample',
 -> dysta2(); obj= 2.75
 resul(),  black() and return() from C.
$objective
[1] 2.75

$i.med
[1] 2 3 4

$medoids
    x   y
2   5  63
21 28 147
45 85 115

$clusinfo
     size max_diss av_diss isolation
[1,]    2       11     5.5 0.1028037
[2,]    1        0     0.0 0.0000000
[3,]    1        0     0.0 0.0000000

$sample
[1] "1"  "2"  "21" "45"

> 
> daisy(ru4, "manhattan")
Dissimilarities :
     1   2  21  22  45  46
2   11                    
21 118 107                
22 124 113   6            
45 143 132  89  87        
46 124 113 108 106  19    
47 115 104 103 101  28   9

Metric :  manhattan 
Number of objects : 7
> ## this one (k=3) gave problems, from ss = 6 on ___ still after 2002-12-28 ___ :
> for(ss in 4:nrow(ru4)){
+     cat("---\n\nsample size = ",ss,"\n")
+     print(clara(ru4,k=3,met="manhattan",sampsize=ss)[clInS])
+ }
---

sample size =  4 
$objective
[1] 7.714286

$i.med
[1] 1 4 7

$medoids
    x   y
1   4  53
22 32 149
47 78  94

$clusinfo
     size max_diss  av_diss  isolation
[1,]    2       11  5.50000 0.09565217
[2,]    2        6  3.00000 0.05940594
[3,]    3       28 12.33333 0.27722772

$sample
[1] "1"  "22" "45" "47"

---

sample size =  5 
$objective
[1] 7.714286

$i.med
[1] 2 3 7

$medoids
    x   y
2   5  63
21 28 147
47 78  94

$clusinfo
     size max_diss  av_diss  isolation
[1,]    2       11  5.50000 0.10576923
[2,]    2        6  3.00000 0.05825243
[3,]    3       28 12.33333 0.27184466

$sample
[1] "2"  "21" "22" "45" "47"

---

sample size =  6 
$objective
[1] 6.428571

$i.med
[1] 2 4 6

$medoids
    x   y
2   5  63
22 32 149
46 85  96

$clusinfo
     size max_diss  av_diss  isolation
[1,]    2       11 5.500000 0.09734513
[2,]    2        6 3.000000 0.05660377
[3,]    3       19 9.333333 0.17924528

$sample
[1] "2"  "21" "22" "45" "46" "47"

---

sample size =  7 
$objective
[1] 6.428571

$i.med
[1] 2 4 6

$medoids
    x   y
2   5  63
22 32 149
46 85  96

$clusinfo
     size max_diss  av_diss  isolation
[1,]    2       11 5.500000 0.09734513
[2,]    2        6 3.000000 0.05660377
[3,]    3       19 9.333333 0.17924528

$sample
[1] "1"  "2"  "21" "22" "45" "46" "47"

> for(ss in 5:nrow(ru3)){
+     cat("---\n\nsample size = ",ss,"\n")
+     print(clara(ru3,k=4,met="manhattan",sampsize=ss)[clInS])
+ }
---

sample size =  5 
$objective
[1] 13.625

$i.med
[1]  4  5 10 15

$medoids
    x   y
4   9  77
21 28 147
45 85 115
62 77  12

$clusinfo
     size max_diss av_diss isolation
[1,]    4       29   16.50 0.3258427
[2,]    5       14    9.00 0.1573034
[3,]    4       30   19.25 0.3370787
[4,]    3       15   10.00 0.1351351

$sample
[1] "3"  "4"  "21" "45" "62"

---

sample size =  6 
$objective
[1] 9.0625

$i.med
[1]  3  7 13 15

$medoids
    x   y
3  10  59
23 35 153
48 74  96
62 77  12

$clusinfo
     size max_diss av_diss isolation
[1,]    4       19   10.00 0.1881188
[2,]    5       13    5.60 0.1354167
[3,]    4       30   11.75 0.3448276
[4,]    3       15   10.00 0.1724138

$sample
[1] "3"  "21" "23" "45" "48" "62"

---

sample size =  7 
$objective
[1] 9.0625

$i.med
[1]  3  7 13 15

$medoids
    x   y
3  10  59
23 35 153
48 74  96
62 77  12

$clusinfo
     size max_diss av_diss isolation
[1,]    4       19   10.00 0.1881188
[2,]    5       13    5.60 0.1354167
[3,]    4       30   11.75 0.3448276
[4,]    3       15   10.00 0.1724138

$sample
[1] "2"  "3"  "21" "23" "45" "48" "62"

---

sample size =  8 
$objective
[1] 8.8125

$i.med
[1]  3  7 12 15

$medoids
    x   y
3  10  59
23 35 153
47 78  94
62 77  12

$clusinfo
     size max_diss av_diss isolation
[1,]    4       19   10.00 0.1844660
[2,]    5       13    5.60 0.1274510
[3,]    4       28   10.75 0.3373494
[4,]    3       15   10.00 0.1807229

$sample
[1] "3"  "21" "23" "46" "47" "48" "61" "62"

---

sample size =  9 
$objective
[1] 9.3125

$i.med
[1]  2  6 11 16

$medoids
    x   y
2   5  63
22 32 149
46 85  96
63 83  21

$clusinfo
     size max_diss av_diss isolation
[1,]    4       18    9.50 0.1592920
[2,]    5        8    5.40 0.0754717
[3,]    4       19    9.75 0.2467532
[4,]    3       30   15.00 0.3896104

$sample
[1] "2"  "21" "22" "23" "45" "46" "47" "61" "63"

---

sample size =  10 
$objective
[1] 8.5625

$i.med
[1]  3  7 11 15

$medoids
    x   y
3  10  59
23 35 153
46 85  96
62 77  12

$clusinfo
     size max_diss av_diss isolation
[1,]    4       19   10.00 0.1696429
[2,]    5       13    5.60 0.1214953
[3,]    4       19    9.75 0.2065217
[4,]    3       15   10.00 0.1630435

$sample
 [1] "2"  "3"  "22" "23" "45" "46" "47" "61" "62" "63"

---

sample size =  11 
$objective
[1] 8.6875

$i.med
[1]  2  7 12 15

$medoids
    x   y
2   5  63
23 35 153
47 78  94
62 77  12

$clusinfo
     size max_diss av_diss isolation
[1,]    4       18    9.50 0.1730769
[2,]    5       13    5.60 0.1274510
[3,]    4       28   10.75 0.3373494
[4,]    3       15   10.00 0.1807229

$sample
 [1] "1"  "2"  "3"  "4"  "23" "24" "25" "45" "47" "48" "62"

---

sample size =  12 
$objective
[1] 8.8125

$i.med
[1]  3  7 12 15

$medoids
    x   y
3  10  59
23 35 153
47 78  94
62 77  12

$clusinfo
     size max_diss av_diss isolation
[1,]    4       19   10.00 0.1844660
[2,]    5       13    5.60 0.1274510
[3,]    4       28   10.75 0.3373494
[4,]    3       15   10.00 0.1807229

$sample
 [1] "2"  "3"  "22" "23" "24" "25" "46" "47" "48" "61" "62" "63"

---

sample size =  13 
$objective
[1] 8.4375

$i.med
[1]  2  7 11 15

$medoids
    x   y
2   5  63
23 35 153
46 85  96
62 77  12

$clusinfo
     size max_diss av_diss isolation
[1,]    4       18    9.50 0.1592920
[2,]    5       13    5.60 0.1214953
[3,]    4       19    9.75 0.2065217
[4,]    3       15   10.00 0.1630435

$sample
 [1] "1"  "2"  "4"  "22" "23" "24" "25" "45" "46" "47" "61" "62" "63"

---

sample size =  14 
$objective
[1] 8.4375

$i.med
[1]  2  7 11 15

$medoids
    x   y
2   5  63
23 35 153
46 85  96
62 77  12

$clusinfo
     size max_diss av_diss isolation
[1,]    4       18    9.50 0.1592920
[2,]    5       13    5.60 0.1214953
[3,]    4       19    9.75 0.2065217
[4,]    3       15   10.00 0.1630435

$sample
 [1] "2"  "3"  "4"  "22" "23" "24" "25" "45" "46" "47" "48" "61" "62" "63"

---

sample size =  15 
$objective
[1] 8.375

$i.med
[1]  2  6 11 15

$medoids
    x   y
2   5  63
22 32 149
46 85  96
62 77  12

$clusinfo
     size max_diss av_diss isolation
[1,]    4       18    9.50 0.1592920
[2,]    5        8    5.40 0.0754717
[3,]    4       19    9.75 0.2065217
[4,]    3       15   10.00 0.1630435

$sample
 [1] "2"  "3"  "4"  "21" "22" "23" "24" "25" "45" "46" "47" "48" "61" "62" "63"

---

sample size =  16 
$objective
[1] 8.375

$i.med
[1]  2  6 11 15

$medoids
    x   y
2   5  63
22 32 149
46 85  96
62 77  12

$clusinfo
     size max_diss av_diss isolation
[1,]    4       18    9.50 0.1592920
[2,]    5        8    5.40 0.0754717
[3,]    4       19    9.75 0.2065217
[4,]    3       15   10.00 0.1630435

$sample
 [1] "1"  "2"  "3"  "4"  "21" "22" "23" "24" "25" "45" "46" "47" "48" "61" "62"
[16] "63"

> 
> ## Last Line:
> cat('Time elapsed: ', proc.time() - .proctime00,'\n')
Time elapsed:  1.4 0.013 1.433 0 0 
> ## Lynne (P IV, 1.6 GHz): 18.81; then (no NA; R 1.9.0-alpha): 15.07
> ## nb-mm (P III,700 MHz): 27.97
> 
> proc.time()
   user  system elapsed 
  1.674   0.102   1.917