library(cluster)
pam_fit <- pam(iris[, 1:4], 5) # Partition Around Medoids
summary(pam_fit) # Get summary
## Medoids:
## ID Sepal.Length Sepal.Width Petal.Length Petal.Width
## [1,] 8 5.0 3.4 1.5 0.2
## [2,] 64 6.1 2.9 4.7 1.4
## [3,] 70 5.6 2.5 3.9 1.1
## [4,] 113 6.8 3.0 5.5 2.1
## [5,] 106 7.6 3.0 6.6 2.1
## 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 2 2 2 3 2 2 2 3 2 3 3 2 3 2 3 2 2 3 2 3 2 3 2 2
## [75] 2 2 2 4 2 3 3 3 3 2 2 2 2 2 3 3 3 2 3 3 3 3 3 2 3 3 4 2 4 4 4 5 3 5 4 5 4
## [112] 4 4 2 2 4 4 5 5 2 4 2 5 2 4 4 2 2 4 4 5 5 4 2 2 5 4 4 2 4 4 4 2 4 4 4 2 4
## [149] 4 2
## Objective function:
## build swap
## 0.5520959 0.5272835
##
## Numerical information per cluster:
## size max_diss av_diss diameter separation
## [1,] 50 1.2369317 0.4846000 2.428992 1.6401219
## [2,] 40 1.1224972 0.5874690 1.661325 0.3000000
## [3,] 24 1.1000000 0.5205001 1.627882 0.3000000
## [4,] 27 0.8660254 0.5077127 1.374773 0.3162278
## [5,] 9 0.9643651 0.5737248 1.389244 0.4358899
##
## Isolated clusters:
## L-clusters: character(0)
## L*-clusters: character(0)
##
## Silhouette plot information:
## cluster neighbor sil_width
## 1 1 3 0.823782713
## 8 1 3 0.822979939
## 18 1 3 0.821191829
## 50 1 3 0.820946901
## 5 1 3 0.819989654
## 41 1 3 0.819878967
## 40 1 3 0.818905711
## 29 1 3 0.812116253
## 38 1 3 0.811246460
## 28 1 3 0.810123090
## 12 1 3 0.799834938
## 36 1 3 0.798299736
## 27 1 3 0.796367017
## 3 1 3 0.793333297
## 22 1 3 0.791981289
## 35 1 3 0.789669653
## 20 1 3 0.786689337
## 10 1 3 0.785945881
## 7 1 3 0.784292003
## 49 1 3 0.784257843
## 48 1 3 0.779752099
## 47 1 3 0.777942371
## 30 1 3 0.775746314
## 2 1 3 0.773966636
## 31 1 3 0.771500461
## 13 1 3 0.768378249
## 11 1 3 0.766919198
## 46 1 3 0.766389968
## 4 1 3 0.760528648
## 32 1 3 0.757686027
## 37 1 3 0.756698504
## 44 1 3 0.756592820
## 23 1 3 0.756110206
## 26 1 3 0.750347697
## 24 1 3 0.745215991
## 43 1 3 0.741959676
## 17 1 3 0.738559175
## 21 1 3 0.737981532
## 33 1 3 0.724060094
## 25 1 3 0.721037402
## 39 1 3 0.718527842
## 6 1 3 0.702644402
## 9 1 3 0.696132723
## 14 1 3 0.695119096
## 45 1 3 0.694338305
## 34 1 3 0.681434226
## 15 1 3 0.660358295
## 19 1 3 0.655738366
## 16 1 3 0.597147161
## 42 1 3 0.555054870
## 64 2 3 0.471339064
## 55 2 4 0.458317613
## 52 2 4 0.443534424
## 92 2 3 0.433142280
## 59 2 4 0.431621613
## 139 2 4 0.417405565
## 76 2 3 0.415948453
## 127 2 4 0.402579655
## 73 2 4 0.399050400
## 66 2 4 0.388387404
## 74 2 3 0.372099711
## 71 2 4 0.366704189
## 57 2 4 0.363901411
## 84 2 4 0.349237676
## 79 2 3 0.347927387
## 128 2 4 0.344414971
## 86 2 3 0.342879650
## 120 2 3 0.339139851
## 122 2 4 0.324164232
## 124 2 4 0.310947933
## 87 2 4 0.296111811
## 75 2 3 0.294712248
## 114 2 4 0.287220191
## 102 2 4 0.276114979
## 143 2 4 0.276114979
## 134 2 4 0.274416839
## 77 2 4 0.262524104
## 150 2 4 0.250702974
## 98 2 3 0.207008254
## 69 2 3 0.206965943
## 147 2 4 0.186361678
## 51 2 4 0.158584073
## 88 2 3 0.154974194
## 67 2 3 0.080197066
## 53 2 4 0.068704364
## 135 2 4 0.009859737
## 115 2 4 0.007411473
## 62 2 3 -0.010322685
## 56 2 3 -0.018480568
## 85 2 3 -0.056548190
## 81 3 2 0.593063391
## 82 3 2 0.588486416
## 70 3 2 0.577829255
## 90 3 2 0.541572577
## 80 3 2 0.540075551
## 94 3 2 0.527900827
## 58 3 2 0.518504963
## 54 3 2 0.510585771
## 60 3 2 0.509830210
## 61 3 2 0.497940985
## 65 3 2 0.497569203
## 83 3 2 0.489146497
## 93 3 2 0.469694889
## 99 3 1 0.458807124
## 68 3 2 0.409714352
## 100 3 2 0.400450702
## 95 3 2 0.396521625
## 89 3 2 0.348880615
## 63 3 2 0.336954121
## 91 3 2 0.295905041
## 97 3 2 0.281018209
## 96 3 2 0.262894448
## 107 3 2 0.186381085
## 72 3 2 0.161825540
## 113 4 2 0.577935875
## 105 4 5 0.577360661
## 141 4 5 0.575917221
## 125 4 5 0.550165260
## 140 4 2 0.541953152
## 133 4 2 0.496625982
## 145 4 5 0.487427796
## 137 4 2 0.486932219
## 129 4 2 0.484485212
## 121 4 5 0.461832625
## 146 4 2 0.455480506
## 116 4 2 0.450650676
## 117 4 2 0.425816992
## 101 4 5 0.425555783
## 142 4 2 0.407031174
## 144 4 5 0.401369810
## 138 4 2 0.394312582
## 149 4 2 0.388080059
## 109 4 2 0.374157348
## 104 4 2 0.351135503
## 148 4 2 0.328372054
## 111 4 2 0.256998908
## 112 4 2 0.192307087
## 103 4 5 0.154141472
## 130 4 5 0.105352310
## 78 4 2 -0.016348526
## 126 4 5 -0.082878191
## 106 5 4 0.565971497
## 123 5 4 0.546749956
## 119 5 4 0.477509712
## 118 5 4 0.459700515
## 132 5 4 0.439388428
## 136 5 4 0.409060616
## 108 5 4 0.312571293
## 131 5 4 0.253594219
## 110 5 4 0.082867697
## Average silhouette width per cluster:
## [1] 0.7575140 0.2733844 0.4333981 0.3797101 0.3941571
## Average silhouette width of total data set:
## [1] 0.4867481
##
## Available components:
## [1] "medoids" "id.med" "clustering" "objective" "isolation"
## [6] "clusinfo" "silinfo" "diss" "call" "data"