##
## TraMineR stable version 2.0-11.1 (Built: 2019-04-24)
## Website: http://traminer.unige.ch
## Please type 'citation("TraMineR")' for citation information.
## ï..Name Reading.1 Reading.2 Reading.3 Reading.4 Reading.5 Reading.6
## 1 Tim 5 5 5 5 5 5
## 2 Tim 3 3 3 3 3 3
## 3 Tim 1 1 1 1 1 1
## Reading.7 Reading.8 Reading.9 Reading.10 Reading.11 Reading.12
## 1 5 5 5 5 5 5
## 2 3 3 3 3 3 3
## 3 1 1 1 1 1 1
## Reading.13 Reading.14 Reading.15 Reading.16 Reading.17 Reading.18
## 1 5 5 7 7 7 7
## 2 3 3 3 3 3 3
## 3 1 1 1 1 1 1
## Reading.19 Reading.20 Reading.21 Reading.22 Reading.23 Reading.24
## 1 7 7 7 7 7 7
## 2 3 3 3 3 3 3
## 3 1 1 1 5 5 5
## Reading.25 Reading.26 Reading.27 Reading.28 Reading.29 Reading.30
## 1 7 7 7 5 5 5
## 2 3 3 3 3 3 3
## 3 5 5 5 5 5 5
## [1] 147 31
## [>] 7 distinct states appear in the data:
## 1 = 1
## 2 = 2
## 3 = 3
## 4 = 4
## 5 = 5
## 6 = 6
## 7 = 7
## [>] state coding:
## [alphabet] [label] [long label]
## 1 1 1 BookShelf
## 2 2 2 PS4
## 3 3 3 Counter
## 4 4 4 Side1
## 5 5 5 Side2
## 6 6 6 Business
## 7 7 7 Corner
## [>] 147 sequences in the data set
## [>] min/max sequence length: 30/30
Generate Distance Matrix using 1) Optimal Matching, 2) Lowest common prefix, 3) Lowest Common Subsequence
#the distance matrix is generated using the transition rates in the data
coffeecc <- seqsubm(cafeData.seq, method = "TRATE")
## [>] creating substitution-cost matrix using transition rates ...
## [>] computing transition probabilities for states 1/2/3/4/5/6/7 ...
#Compute Optimal Matching, Lowest common prefix, Lowest Common Subsequence distance matrix
coffee.OM <- seqdist(cafeData.seq, method = "OM", sm = coffeecc)
## [>] 147 sequences with 7 distinct states
## [>] checking 'sm' (one value for each state, triangle inequality)
## [>] 115 distinct sequences
## [>] min/max sequence length: 30/30
## [>] computing distances using the OM metric
## [>] elapsed time: 0.05 secs
coffee.LCP <- seqdist(cafeData.seq, method = "LCP")
## [>] 147 sequences with 7 distinct states
## [>] 115 distinct sequences
## [>] min/max sequence length: 30/30
## [>] computing distances using the LCP metric
## [>] elapsed time: 0.03 secs
coffee.LCS <- seqdist(cafeData.seq, method = "LCS")
## [>] 147 sequences with 7 distinct states
## [>] creating a 'sm' with a substitution cost of 2
## [>] creating 7x7 substitution-cost matrix using 2 as constant value
## [>] 115 distinct sequences
## [>] min/max sequence length: 30/30
## [>] computing distances using the LCS metric
## [>] elapsed time: 0.05 secs
Native Cluster Using the distance matrices
#Using Optimal Matching, native Cluster
clusterward <- agnes(coffee.OM, diss = TRUE, method = "ward")
cluster6 <- cutree(clusterward, k = 6)
cluster6 <- factor(cluster6, labels = c("Type 1", "Type 2", "Type 3","Type 4","Type 5","Type 6"))
table(cluster6)
## cluster6
## Type 1 Type 2 Type 3 Type 4 Type 5 Type 6
## 38 23 18 22 11 35
#Using LCP , native Cluster
clusterward.LCP <- agnes(coffee.LCP, diss = TRUE, method = "ward")
cluster6.LCP <- cutree(clusterward.LCP, k = 6)
cluster6.LCP <- factor(cluster6.LCP, labels = c("Type 1", "Type 2", "Type 3","Type 4","Type 5","Type 6"))
table(cluster6.LCP)
## cluster6.LCP
## Type 1 Type 2 Type 3 Type 4 Type 5 Type 6
## 19 55 27 13 14 19
#Using LCS , native Cluster
clusterward.LCS <- agnes(coffee.LCS, diss = TRUE, method = "ward")
cluster6.LCS <- cutree(clusterward.LCS, k = 6)
cluster6.LCS <- factor(cluster6.LCS, labels = c("Type 1", "Type 2", "Type 3","Type 4","Type 5","Type 6"))
table(cluster6.LCS)
## cluster6.LCS
## Type 1 Type 2 Type 3 Type 4 Type 5 Type 6
## 38 23 18 22 11 35
K-Medoid Using the distance matrices
#K_Med using OM
K_Med.OM <- pam(coffee.OM, 6, diss = TRUE)
summary(K_Med.OM)
## Medoids:
## ID
## [1,] 128 128
## [2,] 135 135
## [3,] 101 101
## [4,] 21 21
## [5,] 43 43
## [6,] 42 42
## Clustering vector:
## [1] 1 2 3 3 4 4 4 2 1 4 4 1 5 2 5 3 1 4 2 2 4 3 4 5 5 1 5 1 3 6 1 5 6 6 5
## [36] 6 2 5 6 6 6 6 5 1 5 5 2 5 1 1 4 1 4 6 1 1 1 2 2 1 4 1 4 1 1 1 1 2 4 1
## [71] 4 1 2 2 2 2 5 2 2 4 2 2 4 2 2 2 6 2 2 2 6 6 6 4 2 2 2 3 3 3 3 1 2 3 3
## [106] 3 3 1 2 3 1 3 6 3 2 3 3 3 2 2 3 3 6 6 6 6 2 1 2 2 6 6 6 6 2 6 6 6 4 6
## [141] 5 6 6 6 6 6 2
## Objective function:
## build swap
## 18.55174 17.17313
##
## Numerical information per cluster:
## size max_diss av_diss diameter separation
## [1,] 26 35.64905 18.20744 45.52449 21.836134
## [2,] 37 58.89997 18.43465 59.27822 11.840090
## [3,] 21 47.59886 17.93506 47.84957 11.840090
## [4,] 18 37.74167 15.49222 37.91262 11.859340
## [5,] 14 29.72154 16.26248 37.54098 7.967779
## [6,] 31 31.84850 15.67108 37.81522 7.967779
##
## Isolated clusters:
## L-clusters: character(0)
## L*-clusters: character(0)
##
## Silhouette plot information:
## cluster neighbor sil_width
## 17 1 3 0.644619532
## 67 1 3 0.644619532
## 72 1 3 0.644619532
## 111 1 3 0.644619532
## 128 1 2 0.614975466
## 52 1 3 0.563035796
## 44 1 5 0.561945064
## 55 1 2 0.556716961
## 56 1 2 0.546693210
## 62 1 2 0.524318406
## 66 1 2 0.511634796
## 60 1 2 0.496797396
## 70 1 2 0.496797396
## 65 1 2 0.480659045
## 49 1 3 0.467830810
## 102 1 3 0.452666393
## 9 1 3 0.422461440
## 50 1 2 0.403759260
## 57 1 2 0.376970308
## 1 1 2 0.344024118
## 26 1 2 0.344024118
## 12 1 3 0.339966138
## 108 1 3 0.216096788
## 64 1 3 0.170245239
## 31 1 5 -0.021607076
## 28 1 5 -0.024031064
## 2 2 3 0.626130123
## 58 2 3 0.626130123
## 73 2 3 0.626130123
## 75 2 3 0.626130123
## 79 2 3 0.626130123
## 84 2 3 0.626130123
## 88 2 3 0.626130123
## 90 2 3 0.626130123
## 95 2 3 0.626130123
## 97 2 3 0.626130123
## 103 2 3 0.626130123
## 130 2 3 0.626130123
## 135 2 3 0.626130123
## 120 2 3 0.596065962
## 147 2 3 0.575001264
## 8 2 3 0.552895441
## 74 2 5 0.427989945
## 85 2 5 0.389700323
## 81 2 3 0.381248661
## 119 2 1 0.381163856
## 109 2 5 0.318950085
## 19 2 3 0.267215765
## 78 2 3 0.212830841
## 127 2 3 0.212830841
## 20 2 3 0.211848973
## 82 2 3 0.192747124
## 89 2 1 0.185127786
## 59 2 4 0.141320608
## 14 2 3 0.133855021
## 115 2 3 0.119015062
## 37 2 5 0.087441366
## 96 2 3 0.059849412
## 68 2 6 0.058665837
## 86 2 3 0.055525789
## 47 2 3 0.055510195
## 129 2 6 0.026721698
## 76 2 3 -0.041340950
## 101 3 4 0.628215039
## 107 3 4 0.628215039
## 114 3 4 0.628215039
## 117 3 4 0.628215039
## 122 3 4 0.628215039
## 16 3 4 0.599125727
## 118 3 4 0.579355400
## 112 3 2 0.577223850
## 3 3 4 0.554552032
## 104 3 6 0.530816353
## 121 3 4 0.502814129
## 116 3 2 0.456566724
## 106 3 4 0.447868023
## 4 3 4 0.429660863
## 100 3 2 0.379968165
## 99 3 4 0.300023566
## 98 3 2 0.182096356
## 29 3 5 0.117502299
## 105 3 2 0.094111279
## 22 3 2 0.075788560
## 110 3 1 0.022699377
## 10 4 3 0.703163918
## 63 4 3 0.703163918
## 80 4 3 0.703163918
## 51 4 2 0.685303772
## 83 4 2 0.655218184
## 21 4 3 0.652120254
## 139 4 3 0.622551525
## 61 4 3 0.615660968
## 94 4 2 0.610166553
## 11 4 3 0.578901110
## 53 4 3 0.569089928
## 23 4 3 0.486490879
## 7 4 3 0.461838585
## 69 4 2 0.403910860
## 18 4 3 0.375730948
## 6 4 3 0.345724848
## 71 4 2 0.321400595
## 5 4 2 0.259081258
## 25 5 2 0.648956593
## 27 5 2 0.648956593
## 35 5 2 0.648956593
## 141 5 2 0.634070150
## 43 5 2 0.599597514
## 15 5 3 0.492619968
## 32 5 6 0.474645018
## 13 5 2 0.468145673
## 48 5 2 0.456681203
## 38 5 2 0.388726450
## 46 5 6 0.332819032
## 24 5 6 0.089395208
## 45 5 6 0.007037486
## 77 5 2 -0.094694916
## 91 6 5 0.682983857
## 131 6 5 0.682983857
## 138 6 5 0.682983857
## 140 6 5 0.682983857
## 142 6 5 0.682983857
## 144 6 5 0.682983857
## 146 6 5 0.682983857
## 42 6 2 0.668400846
## 92 6 2 0.667396701
## 124 6 5 0.665845440
## 134 6 5 0.662738067
## 137 6 5 0.630944627
## 132 6 2 0.618163668
## 54 6 3 0.584357590
## 93 6 2 0.570086122
## 33 6 3 0.559177119
## 30 6 5 0.557714359
## 136 6 2 0.555369756
## 123 6 2 0.525163395
## 125 6 3 0.507911026
## 39 6 3 0.472560514
## 36 6 3 0.418160079
## 145 6 2 0.407781401
## 133 6 2 0.398221030
## 87 6 2 0.366250591
## 126 6 3 0.314363376
## 143 6 3 0.304761470
## 113 6 3 0.297494971
## 40 6 5 0.289086669
## 41 6 5 0.283176584
## 34 6 5 0.172310332
## Average silhouette width per cluster:
## [1] 0.4394022 0.3714020 0.4281547 0.5418157 0.4139938 0.5251072
## Average silhouette width of total data set:
## [1] 0.4488741
##
## Available components:
## [1] "medoids" "id.med" "clustering" "objective" "isolation"
## [6] "clusinfo" "silinfo" "diss" "call"
plot(K_Med.OM)
clusplot(coffee.OM,K_Med.OM$clustering, diss = TRUE, shade = TRUE)
#K_Med using LCP
K_Med.LCP <- pam(coffee.LCP, 6, diss = TRUE)
summary(K_Med.LCP)
## Medoids:
## ID
## [1,] 35 35
## [2,] 135 135
## [3,] 122 122
## [4,] 80 80
## [5,] 111 111
## [6,] 146 146
## Clustering vector:
## [1] 1 2 3 2 1 3 3 2 3 4 4 5 2 2 3 3 5 3 1 2 4 3 2 2 1 1 1 1 3 6 2 1 3 3 1
## [36] 3 2 1 3 6 1 6 2 1 6 1 2 3 3 5 2 3 3 4 2 5 2 2 4 5 4 5 4 3 5 2 5 6 2 5
## [71] 4 5 2 2 2 2 2 2 2 4 2 2 4 2 2 1 2 2 2 2 6 2 2 4 2 2 2 2 2 2 3 5 2 6 3
## [106] 2 3 3 2 1 5 3 2 3 1 2 3 3 5 2 2 3 2 6 2 6 2 5 6 2 6 6 2 6 2 4 1 6 4 6
## [141] 1 6 6 6 1 6 2
## Objective function:
## build swap
## 31.86395 31.86395
##
## Numerical information per cluster:
## size max_diss av_diss diameter separation
## [1,] 19 60 43.05263 60 60
## [2,] 55 54 33.30909 54 60
## [3,] 27 52 31.85185 52 60
## [4,] 13 54 31.23077 54 60
## [5,] 14 52 23.71429 52 60
## [6,] 19 52 22.94737 52 60
##
## Isolated clusters:
## L-clusters: character(0)
## L*-clusters: [1] 2 3 4 5 6
##
## Silhouette plot information:
## cluster neighbor sil_width
## 25 1 2 0.24259259
## 27 1 2 0.24259259
## 35 1 2 0.24259259
## 32 1 2 0.21296296
## 1 1 2 0.19629630
## 26 1 2 0.19629630
## 5 1 2 0.18703704
## 19 1 2 0.18703704
## 86 1 2 0.18703704
## 46 1 2 0.18518519
## 115 1 2 0.17222222
## 41 1 2 0.12592593
## 38 1 2 0.12037037
## 28 1 2 0.11666667
## 141 1 2 0.09444444
## 137 1 2 0.08703704
## 44 1 2 0.08148148
## 145 1 2 0.07407407
## 110 1 2 0.06666667
## 2 2 1 0.43456790
## 58 2 1 0.43456790
## 73 2 1 0.43456790
## 75 2 1 0.43456790
## 79 2 1 0.43456790
## 84 2 1 0.43456790
## 88 2 1 0.43456790
## 90 2 1 0.43456790
## 95 2 1 0.43456790
## 97 2 1 0.43456790
## 103 2 1 0.43456790
## 130 2 1 0.43456790
## 135 2 1 0.43456790
## 37 2 1 0.35308642
## 82 2 1 0.32901235
## 76 2 1 0.32037037
## 85 2 1 0.32037037
## 20 2 1 0.31049383
## 74 2 1 0.30000000
## 81 2 1 0.30000000
## 87 2 1 0.30000000
## 57 2 1 0.28765432
## 13 2 1 0.27469136
## 47 2 1 0.27469136
## 100 2 1 0.27469136
## 77 2 1 0.25987654
## 147 2 1 0.25987654
## 109 2 1 0.24382716
## 78 2 1 0.24074074
## 127 2 1 0.24074074
## 98 2 1 0.22716049
## 69 2 1 0.21172840
## 120 2 1 0.21172840
## 43 2 1 0.21111111
## 66 2 1 0.21111111
## 89 2 1 0.21111111
## 113 2 1 0.20864198
## 92 2 1 0.18827160
## 123 2 1 0.18827160
## 93 2 1 0.18765432
## 8 2 1 0.18641975
## 14 2 1 0.18641975
## 55 2 1 0.18641975
## 106 2 1 0.18641975
## 24 2 1 0.18395062
## 4 2 1 0.16851852
## 125 2 1 0.16851852
## 23 2 1 0.16543210
## 31 2 1 0.16172840
## 96 2 1 0.13456790
## 133 2 1 0.13456790
## 99 2 1 0.13209877
## 116 2 1 0.13209877
## 51 2 1 0.10000000
## 121 2 1 0.10000000
## 101 3 1 0.44871795
## 107 3 1 0.44871795
## 114 3 1 0.44871795
## 117 3 1 0.44871795
## 122 3 1 0.44871795
## 16 3 1 0.42820513
## 3 3 1 0.39615385
## 105 3 1 0.35000000
## 108 3 1 0.34102564
## 118 3 1 0.32051282
## 64 3 1 0.31538462
## 112 3 1 0.31538462
## 9 3 1 0.30897436
## 33 3 1 0.29487179
## 36 3 1 0.29487179
## 6 3 1 0.29358974
## 29 3 1 0.29358974
## 49 3 1 0.29358974
## 15 3 1 0.26282051
## 34 3 1 0.26282051
## 7 3 1 0.25256410
## 39 3 1 0.25256410
## 18 3 1 0.22564103
## 48 3 1 0.20256410
## 53 3 1 0.20256410
## 22 3 1 0.19743590
## 52 3 1 0.13333333
## 10 4 1 0.43611111
## 63 4 1 0.43611111
## 80 4 1 0.43611111
## 71 4 1 0.36944444
## 21 4 1 0.33611111
## 59 4 1 0.31388889
## 11 4 1 0.30555556
## 61 4 1 0.30555556
## 83 4 1 0.28611111
## 139 4 1 0.24166667
## 94 4 1 0.21666667
## 136 4 1 0.16111111
## 54 4 1 0.10000000
## 17 5 1 0.57435897
## 67 5 1 0.57435897
## 72 5 1 0.57435897
## 111 5 1 0.57435897
## 56 5 1 0.52820513
## 60 5 1 0.52820513
## 70 5 1 0.52820513
## 12 5 1 0.40000000
## 65 5 1 0.38205128
## 62 5 1 0.34102564
## 102 5 1 0.24871795
## 119 5 1 0.24871795
## 128 5 1 0.16410256
## 50 5 1 0.13333333
## 91 6 1 0.59629630
## 131 6 1 0.59629630
## 138 6 1 0.59629630
## 140 6 1 0.59629630
## 142 6 1 0.59629630
## 144 6 1 0.59629630
## 146 6 1 0.59629630
## 30 6 1 0.51851852
## 40 6 1 0.46666667
## 45 6 1 0.40740741
## 134 6 1 0.40740741
## 68 6 1 0.38148148
## 132 6 1 0.38148148
## 129 6 1 0.34814815
## 124 6 1 0.27592593
## 143 6 1 0.25000000
## 104 6 1 0.19444444
## 126 6 1 0.16481481
## 42 6 1 0.13333333
## Average silhouette width per cluster:
## [1] 0.1588694 0.2713356 0.3141500 0.3034188 0.4142857 0.4265107
## Average silhouette width of total data set:
## [1] 0.3011713
##
## Available components:
## [1] "medoids" "id.med" "clustering" "objective" "isolation"
## [6] "clusinfo" "silinfo" "diss" "call"
plot(K_Med.LCP)
clusplot(coffee.LCP,K_Med.LCP$clustering, diss = TRUE, shade = TRUE)
#K_Med using LCS
K_Med.LCS <- pam(coffee.LCS, 6, diss = TRUE)
summary(K_Med.LCS)
## Medoids:
## ID
## [1,] 128 128
## [2,] 135 135
## [3,] 101 101
## [4,] 21 21
## [5,] 141 141
## [6,] 42 42
## Clustering vector:
## [1] 1 2 3 3 4 4 4 2 1 4 4 1 5 2 5 3 1 4 2 2 4 3 4 6 5 1 5 5 3 6 1 5 6 6 5
## [36] 6 2 5 6 6 6 6 5 1 6 5 2 5 1 1 4 1 4 6 1 1 1 2 2 1 4 1 4 1 1 1 1 2 4 1
## [71] 4 1 2 2 2 2 2 2 2 4 2 2 4 2 2 4 6 2 2 2 6 6 6 4 2 1 2 3 3 3 3 1 2 3 3
## [106] 3 3 1 2 3 1 3 6 3 6 3 3 3 2 2 3 3 6 6 6 6 2 1 2 2 6 6 6 6 2 6 6 6 4 6
## [141] 5 6 6 6 6 6 2
## Objective function:
## build swap
## 18.77551 17.38776
##
## Numerical information per cluster:
## size max_diss av_diss diameter separation
## [1,] 26 44 18.69231 52 22
## [2,] 35 44 16.11429 44 12
## [3,] 21 48 18.19048 48 12
## [4,] 19 60 18.00000 60 8
## [5,] 12 34 15.33333 38 16
## [6,] 34 52 17.58824 60 8
##
## Isolated clusters:
## L-clusters: character(0)
## L*-clusters: character(0)
##
## Silhouette plot information:
## cluster neighbor sil_width
## 17 1 5 0.638300283
## 67 1 5 0.638300283
## 72 1 5 0.638300283
## 111 1 5 0.638300283
## 128 1 2 0.607159353
## 52 1 3 0.557983871
## 55 1 2 0.550617284
## 56 1 2 0.545169082
## 44 1 5 0.536842105
## 62 1 2 0.507509881
## 66 1 2 0.502583979
## 60 1 2 0.491578947
## 70 1 2 0.491578947
## 65 1 2 0.460797799
## 49 1 3 0.453207547
## 102 1 3 0.448057554
## 9 1 3 0.406814815
## 1 1 2 0.370971185
## 26 1 2 0.370971185
## 50 1 2 0.370000000
## 57 1 2 0.341562500
## 12 1 3 0.341507538
## 108 1 3 0.195897436
## 64 1 3 0.157735849
## 31 1 5 -0.031316348
## 96 1 2 -0.079621096
## 2 2 3 0.678047189
## 58 2 3 0.678047189
## 73 2 3 0.678047189
## 75 2 3 0.678047189
## 79 2 3 0.678047189
## 84 2 3 0.678047189
## 88 2 3 0.678047189
## 90 2 3 0.678047189
## 95 2 3 0.678047189
## 97 2 3 0.678047189
## 103 2 3 0.678047189
## 130 2 3 0.678047189
## 135 2 3 0.678047189
## 120 2 3 0.635080418
## 147 2 3 0.607171636
## 8 2 3 0.581750811
## 74 2 5 0.508951407
## 85 2 5 0.475844806
## 119 2 1 0.438533348
## 81 2 3 0.435547425
## 109 2 5 0.409654445
## 82 2 3 0.258823529
## 19 2 3 0.251149358
## 59 2 4 0.232857640
## 20 2 3 0.231372549
## 89 2 1 0.225579161
## 37 2 5 0.204044118
## 78 2 3 0.180946292
## 127 2 3 0.180946292
## 77 2 5 0.146901541
## 68 2 6 0.144144144
## 14 2 3 0.138888889
## 129 2 6 0.097938144
## 47 2 3 0.062880325
## 76 2 3 0.018452604
## 101 3 4 0.634173387
## 107 3 4 0.634173387
## 114 3 4 0.634173387
## 117 3 4 0.634173387
## 122 3 4 0.634173387
## 16 3 2 0.602272727
## 118 3 4 0.581524008
## 112 3 2 0.574382716
## 3 3 4 0.554000000
## 104 3 6 0.535189873
## 121 3 4 0.511926606
## 106 3 4 0.463250000
## 4 3 4 0.449347258
## 116 3 2 0.443061840
## 100 3 2 0.358239095
## 99 3 2 0.293269231
## 98 3 2 0.145204263
## 29 3 5 0.107142857
## 22 3 4 0.094186047
## 105 3 2 0.045164234
## 110 3 1 0.008474576
## 10 4 3 0.662835249
## 63 4 3 0.662835249
## 80 4 3 0.662835249
## 51 4 2 0.639067716
## 83 4 2 0.604787715
## 21 4 3 0.604166667
## 139 4 3 0.576180971
## 61 4 3 0.564074874
## 94 4 2 0.554296110
## 11 4 3 0.525959368
## 53 4 3 0.519313305
## 23 4 3 0.438073394
## 7 4 3 0.405000000
## 69 4 2 0.352802216
## 18 4 3 0.313535912
## 5 4 2 0.306453544
## 6 4 3 0.285256410
## 71 4 2 0.244515216
## 86 4 2 -0.069718698
## 25 5 6 0.678571429
## 27 5 6 0.678571429
## 35 5 6 0.678571429
## 141 5 2 0.659223198
## 43 5 2 0.599107789
## 15 5 3 0.523771633
## 32 5 6 0.514898990
## 13 5 2 0.425646239
## 48 5 2 0.421895006
## 38 5 2 0.367936118
## 46 5 6 0.364942529
## 28 5 1 0.021120294
## 91 6 5 0.661660079
## 131 6 5 0.661660079
## 138 6 5 0.661660079
## 140 6 5 0.661660079
## 142 6 5 0.661660079
## 144 6 5 0.661660079
## 146 6 5 0.661660079
## 124 6 2 0.636159804
## 42 6 2 0.631254405
## 92 6 2 0.630021142
## 134 6 2 0.628488273
## 137 6 2 0.615516650
## 132 6 2 0.579661647
## 30 6 5 0.556388629
## 54 6 3 0.540835181
## 93 6 2 0.524049358
## 136 6 2 0.522856931
## 33 6 3 0.508394758
## 123 6 2 0.476624005
## 125 6 3 0.472429210
## 39 6 3 0.433855799
## 36 6 3 0.394077449
## 145 6 2 0.384309194
## 40 6 5 0.355661882
## 133 6 2 0.353595123
## 87 6 2 0.305120167
## 126 6 3 0.285072952
## 143 6 3 0.279095301
## 113 6 3 0.268181818
## 41 6 5 0.247096516
## 34 6 5 0.165682802
## 45 6 5 0.037549407
## 24 6 5 -0.012820513
## 115 6 2 -0.156226296
## Average silhouette width per cluster:
## [1] 0.4288773 0.4366306 0.4255953 0.4659090 0.4945213 0.4498398
## Average silhouette width of total data set:
## [1] 0.4452481
##
## Available components:
## [1] "medoids" "id.med" "clustering" "objective" "isolation"
## [6] "clusinfo" "silinfo" "diss" "call"
plot(K_Med.LCS)
clusplot(coffee.LCS,K_Med.LCS$clustering, diss = TRUE, shade = TRUE)
DB Scan and Compate clusters using Dunn and Calinski
#Note that I tried different values for eps and minPts but not sure whats right. 100 and 5 as shown gave the the most sensible results
#dbscan(coffee.OM, 100, minPts = 5)
#plot(coffee.OM, col=DB$cluster)
intCriteria(coffee.OM,K_Med.OM$clustering,c("C_index","Calinski_Harabasz","Dunn"))
## $c_index
## [1] 0.06378361
##
## $calinski_harabasz
## [1] 64.58252
##
## $dunn
## [1] 0.1499339
intCriteria(coffee.LCP,K_Med.LCP$clustering,c("C_index","Calinski_Harabasz","Dunn"))
## $c_index
## [1] 0.09975508
##
## $calinski_harabasz
## [1] 50.31362
##
## $dunn
## [1] 0.4136939
intCriteria(coffee.LCS,K_Med.LCS$clustering,c("C_index","Calinski_Harabasz","Dunn"))
## $c_index
## [1] 0.05551035
##
## $calinski_harabasz
## [1] 65.11953
##
## $dunn
## [1] 0.1903524