1495 lines
40 KiB
Plaintext
1495 lines
40 KiB
Plaintext
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R Under development (unstable) (2023-10-19 r85354) -- "Unsuffered Consequences"
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Copyright (C) 2023 The R Foundation for Statistical Computing
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Platform: x86_64-pc-linux-gnu
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R is free software and comes with ABSOLUTELY NO WARRANTY.
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You are welcome to redistribute it under certain conditions.
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Type 'license()' or 'licence()' for distribution details.
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R is a collaborative project with many contributors.
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Type 'contributors()' for more information and
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'citation()' on how to cite R or R packages in publications.
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Type 'demo()' for some demos, 'help()' for on-line help, or
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'help.start()' for an HTML browser interface to help.
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Type 'q()' to quit R.
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> library(cluster)
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>
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> ## generate 1500 objects, divided into 2 clusters.
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> suppressWarnings(RNGversion("3.5.0")) # << as long as we don't have R >= 3.6.0
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> set.seed(144)
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> x <- rbind(cbind(rnorm(700, 0,8), rnorm(700, 0,8)),
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+ cbind(rnorm(800,50,8), rnorm(800,10,8)))
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>
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> isEq <- function(x,y, epsF = 100)
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+ is.logical(r <- all.equal(x,y, tol = epsF * .Machine$double.eps)) && r
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>
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> .proctime00 <- proc.time()
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>
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> ## full size sample {should be = pam()}:
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> n0 <- length(iSml <- c(1:70, 701:720))
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> summary(clara0 <- clara(x[iSml,], k = 2, sampsize = n0))
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Object of class 'clara' from call:
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clara(x = x[iSml, ], k = 2, sampsize = n0)
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Medoids:
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[,1] [,2]
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[1,] -1.499522 -1.944452
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[2,] 48.629631 12.998515
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Objective function: 10.23588
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Numerical information per cluster:
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size max_diss av_diss isolation
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[1,] 70 24.81995 10.25745 0.4744879
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[2,] 20 19.07782 10.16040 0.3647145
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Average silhouette width per cluster:
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[1] 0.7144587 0.7090915
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Average silhouette width of best sample: 0.713266
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Best sample:
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[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
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[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
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[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
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[76] 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
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Clustering vector:
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[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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[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
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[77] 2 2 2 2 2 2 2 2 2 2 2 2 2 2
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Silhouette plot information for best sample:
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cluster neighbor sil_width
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45 1 2 0.8033727
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60 1 2 0.8021017
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55 1 2 0.8005931
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66 1 2 0.8002776
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58 1 2 0.7991899
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11 1 2 0.7991773
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41 1 2 0.7973302
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26 1 2 0.7962397
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63 1 2 0.7962229
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13 1 2 0.7949705
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67 1 2 0.7942590
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54 1 2 0.7936184
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17 1 2 0.7916087
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16 1 2 0.7913570
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39 1 2 0.7912755
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6 1 2 0.7840455
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34 1 2 0.7833568
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49 1 2 0.7819733
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9 1 2 0.7789087
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23 1 2 0.7785009
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32 1 2 0.7757325
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22 1 2 0.7655369
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61 1 2 0.7639754
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12 1 2 0.7639644
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5 1 2 0.7606436
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18 1 2 0.7579145
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56 1 2 0.7566307
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3 1 2 0.7537894
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24 1 2 0.7531180
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50 1 2 0.7517817
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48 1 2 0.7501998
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25 1 2 0.7499655
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59 1 2 0.7472022
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19 1 2 0.7445038
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65 1 2 0.7398395
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28 1 2 0.7377377
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38 1 2 0.7370935
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7 1 2 0.7335940
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40 1 2 0.7310012
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14 1 2 0.7294895
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62 1 2 0.7254478
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70 1 2 0.7163214
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4 1 2 0.7157257
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21 1 2 0.7148663
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64 1 2 0.7108496
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2 1 2 0.7062831
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15 1 2 0.7015120
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52 1 2 0.6978313
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37 1 2 0.6954023
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31 1 2 0.6932905
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33 1 2 0.6888478
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10 1 2 0.6805028
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20 1 2 0.6766854
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43 1 2 0.6761461
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8 1 2 0.6749706
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27 1 2 0.6671817
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35 1 2 0.6632888
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68 1 2 0.6587599
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30 1 2 0.6554989
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36 1 2 0.6228481
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53 1 2 0.6203313
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57 1 2 0.6191666
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42 1 2 0.6142020
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47 1 2 0.6024151
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1 1 2 0.5814464
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69 1 2 0.5091186
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46 1 2 0.4961302
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44 1 2 0.4849961
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29 1 2 0.4569316
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51 1 2 0.4230181
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81 2 1 0.7965942
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71 2 1 0.7961971
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85 2 1 0.7919593
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74 2 1 0.7869047
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82 2 1 0.7795304
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78 2 1 0.7788873
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79 2 1 0.7729041
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72 2 1 0.7492980
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88 2 1 0.7447973
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87 2 1 0.7404399
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76 2 1 0.7352351
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77 2 1 0.7216838
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86 2 1 0.7165677
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84 2 1 0.6952406
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73 2 1 0.6942882
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83 2 1 0.6621568
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80 2 1 0.6368446
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90 2 1 0.5743228
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75 2 1 0.5597232
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89 2 1 0.4482549
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4005 dissimilarities, summarized :
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Min. 1st Qu. Median Mean 3rd Qu. Max.
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0.1865 11.5850 20.0580 27.8150 45.5780 85.2320
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Metric : euclidean
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Number of objects : 90
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Available components:
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[1] "sample" "medoids" "i.med" "clustering" "objective"
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[6] "clusinfo" "diss" "call" "silinfo" "data"
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> pam0 <- pam (x[iSml,], k = 2)
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> stopifnot(identical(clara0$clustering, pam0$clustering)
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+ , isEq(clara0$objective, unname(pam0$objective[2]))
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+ )
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>
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> summary(clara2 <- clara(x, 2))
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Object of class 'clara' from call:
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clara(x = x, k = 2)
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Medoids:
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[,1] [,2]
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[1,] 2.012828 -1.896095
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[2,] 51.494628 10.274769
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Objective function: 10.23445
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Numerical information per cluster:
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size max_diss av_diss isolation
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[1,] 700 36.84408 10.49814 0.7230478
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[2,] 800 30.89896 10.00373 0.6063775
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Average silhouette width per cluster:
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[1] 0.7562366 0.7203254
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Average silhouette width of best sample: 0.733384
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Best sample:
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[1] 21 23 50 97 142 168 191 192 197 224 325 328 433 458 471
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[16] 651 712 714 722 797 805 837 909 919 926 999 1006 1018 1019 1049
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[31] 1081 1084 1132 1144 1150 1201 1207 1250 1291 1307 1330 1374 1426 1428
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Clustering vector:
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[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[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
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[1481] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
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Silhouette plot information for best sample:
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cluster neighbor sil_width
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325 1 2 0.8261589
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191 1 2 0.8206687
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23 1 2 0.8149640
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97 1 2 0.8048084
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433 1 2 0.8017745
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458 1 2 0.8008324
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471 1 2 0.7958547
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328 1 2 0.7689099
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142 1 2 0.7619508
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21 1 2 0.7607528
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197 1 2 0.7606641
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50 1 2 0.7509131
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192 1 2 0.7098473
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651 1 2 0.7035969
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224 1 2 0.6843886
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168 1 2 0.5337006
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1084 2 1 0.8180447
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1081 2 1 0.8171686
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1201 2 1 0.8170847
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1291 2 1 0.8167148
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1307 2 1 0.8166841
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1144 2 1 0.8159947
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999 2 1 0.8135303
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1426 2 1 0.8023538
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1049 2 1 0.8022891
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1250 2 1 0.8014300
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712 2 1 0.7859324
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837 2 1 0.7792784
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1018 2 1 0.7764837
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919 2 1 0.7651939
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1374 2 1 0.7648534
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1428 2 1 0.7516819
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1330 2 1 0.7505861
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1006 2 1 0.7368113
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714 2 1 0.7237565
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1150 2 1 0.7046060
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1132 2 1 0.6940608
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909 2 1 0.6859682
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926 2 1 0.6725631
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722 2 1 0.6572791
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797 2 1 0.6395698
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1019 2 1 0.6083662
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805 2 1 0.2814164
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1207 2 1 0.2694097
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946 dissimilarities, summarized :
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Min. 1st Qu. Median Mean 3rd Qu. Max.
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0.4846 12.3230 26.4990 32.2130 52.3910 77.1750
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Metric : euclidean
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Number of objects : 44
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Available components:
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[1] "sample" "medoids" "i.med" "clustering" "objective"
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[6] "clusinfo" "diss" "call" "silinfo" "data"
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>
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> clInd <- c("objective", "i.med", "medoids", "clusinfo")
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> clInS <- c(clInd, "sample")
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> ## clara() {as original code} always draws the *same* random samples !!!!
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> clara(x, 2, samples = 50)[clInd]
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$objective
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[1] 10.06735
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$i.med
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[1] 177 1115
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$medoids
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[,1] [,2]
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[1,] -0.2538744 -1.209148
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[2,] 50.0372683 9.501125
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$clusinfo
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size max_diss av_diss isolation
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[1,] 700 34.67208 10.193945 0.6743054
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[2,] 800 29.51964 9.956571 0.5741003
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> for(i in 1:20)
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+ print(clara(x[sample(nrow(x)),], 2, samples = 50)[clInd])
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$objective
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[1] 10.05727
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$i.med
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[1] 936 192
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$medoids
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[,1] [,2]
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[1,] 50.03726827 9.501124850
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[2,] -0.03900399 -0.009078886
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$clusinfo
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size max_diss av_diss isolation
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[1,] 800 29.51964 9.956571 0.5791419
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[2,] 700 34.06055 10.172348 0.6682295
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$objective
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[1] 10.05296
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$i.med
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[1] 468 1394
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$medoids
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[,1] [,2]
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[1,] -0.3292826 -0.2398794
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[2,] 50.0372683 9.5011249
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$clusinfo
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size max_diss av_diss isolation
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[1,] 700 33.98451 10.163128 0.6624677
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[2,] 800 29.51964 9.956571 0.5754330
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$objective
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[1] 10.05852
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$i.med
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[1] 1171 379
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$medoids
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[,1] [,2]
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[1,] 50.9444060 9.6723175
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[2,] -0.3292826 -0.2398794
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$clusinfo
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size max_diss av_diss isolation
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[1,] 800 30.10388 9.966988 0.5764486
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[2,] 700 33.98451 10.163128 0.6507574
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$objective
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[1] 10.07051
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$i.med
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[1] 75 1254
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$medoids
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[,1] [,2]
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[1,] -0.9493373 0.3552542
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[2,] 50.5455985 9.3904972
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$clusinfo
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size max_diss av_diss isolation
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[1,] 700 33.12704 10.191999 0.6336273
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[2,] 800 29.66384 9.964205 0.5673860
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$objective
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[1] 10.0613
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$i.med
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[1] 199 134
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$medoids
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[,1] [,2]
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[1,] -0.03900399 -0.009078886
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[2,] 49.59384120 9.792964832
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$clusinfo
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size max_diss av_diss isolation
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[1,] 700 34.06055 10.172348 0.6732466
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[2,] 800 29.57491 9.964138 0.5845827
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$objective
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[1] 10.06101
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$i.med
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[1] 1453 1122
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$medoids
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[,1] [,2]
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[1,] 50.0372683 9.50112485
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[2,] -0.9691441 0.03342515
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$clusinfo
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size max_diss av_diss isolation
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[1,] 800 29.51964 9.956571 0.5690241
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[2,] 700 33.31923 10.180359 0.6422655
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$objective
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[1] 10.08603
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$i.med
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[1] 613 318
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$medoids
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[,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
|