R Markdown
library(readr)
seeds_dataset_3_ <- read_delim("C:/Users/lazarus/Downloads/seeds_dataset (3).txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)
## Rows: 209 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (1): 1
## dbl (7): 15.26, 14.84, 0.871, 5.763, 3.312, 2.221, 5.22
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
View(seeds_dataset_3_)
sd <- seeds_dataset_3_
str(sd)
## spc_tbl_ [209 × 8] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ 15.26: num [1:209] 14.9 14.3 13.8 16.1 14.4 ...
## $ 14.84: num [1:209] 14.6 14.1 13.9 15 14.2 ...
## $ 0.871: num [1:209] 0.881 0.905 0.895 0.903 0.895 ...
## $ 5.763: num [1:209] 5.55 5.29 5.32 5.66 5.39 ...
## $ 3.312: num [1:209] 3.33 3.34 3.38 3.56 3.31 ...
## $ 2.221: num [1:209] 1.02 2.7 2.26 1.35 2.46 ...
## $ 5.22 : num [1:209] 4.96 4.83 4.8 5.17 4.96 ...
## $ 1 : chr [1:209] "1" "1" "1" "1" ...
## - attr(*, "spec")=
## .. cols(
## .. `15.26` = col_double(),
## .. `14.84` = col_double(),
## .. `0.871` = col_double(),
## .. `5.763` = col_double(),
## .. `3.312` = col_double(),
## .. `2.221` = col_double(),
## .. `5.22` = col_double(),
## .. `1` = col_character()
## .. )
## - attr(*, "problems")=<externalptr>
feature_name<- c('area', 'perimeter','compactness','length_of_kernel','width_of_kernel','asymetry_coefficient','length_of_kernel_groove', 'type_of_seed')
colnames(sd)<-feature_name
View(sd)
summary(sd)
## area perimeter compactness length_of_kernel
## Min. :10.59 Min. :12.41 Min. :0.8081 Min. :0.8189
## 1st Qu.:12.26 1st Qu.:13.45 1st Qu.:0.8576 1st Qu.:5.2430
## Median :14.34 Median :14.29 Median :0.8740 Median :5.5160
## Mean :14.85 Mean :14.56 Mean :0.8713 Mean :5.5630
## 3rd Qu.:17.32 3rd Qu.:15.73 3rd Qu.:0.8878 3rd Qu.:5.9800
## Max. :21.18 Max. :17.25 Max. :0.9183 Max. :6.6750
## NA's :3
## width_of_kernel asymetry_coefficient length_of_kernel_groove
## Min. :2.630 Min. :0.7651 Min. :3.485
## 1st Qu.:2.955 1st Qu.:2.6400 1st Qu.:5.045
## Median :3.244 Median :3.6000 Median :5.228
## Mean :3.281 Mean :3.7006 Mean :5.408
## 3rd Qu.:3.568 3rd Qu.:4.7730 3rd Qu.:5.879
## Max. :5.325 Max. :8.4560 Max. :6.735
## NA's :1 NA's :4
## type_of_seed
## Length:209
## Class :character
## Mode :character
##
##
##
##
any(is.na(sd))
## [1] TRUE
sd<-na.omit(sd)
str(sd)
## tibble [202 × 8] (S3: tbl_df/tbl/data.frame)
## $ area : num [1:202] 14.9 14.3 13.8 16.1 14.4 ...
## $ perimeter : num [1:202] 14.6 14.1 13.9 15 14.2 ...
## $ compactness : num [1:202] 0.881 0.905 0.895 0.903 0.895 ...
## $ length_of_kernel : num [1:202] 5.55 5.29 5.32 5.66 5.39 ...
## $ width_of_kernel : num [1:202] 3.33 3.34 3.38 3.56 3.31 ...
## $ asymetry_coefficient : num [1:202] 1.02 2.7 2.26 1.35 2.46 ...
## $ length_of_kernel_groove: num [1:202] 4.96 4.83 4.8 5.17 4.96 ...
## $ type_of_seed : chr [1:202] "1" "1" "1" "1" ...
## - attr(*, "na.action")= 'omit' Named int [1:7] 7 35 60 135 169 170 201
## ..- attr(*, "names")= chr [1:7] "7" "35" "60" "135" ...
sd<-sd[,-8]
View(sd)
df_sc<-as.data.frame(scale(sd))
str(df_sc)
## 'data.frame': 202 obs. of 7 variables:
## $ area : num -0.00846 -0.21003 -0.36377 0.42202 -0.17928 ...
## $ perimeter : num -0.0133 -0.3789 -0.4931 0.3066 -0.2875 ...
## $ compactness : num 0.435 1.456 1.05 1.388 1.033 ...
## $ length_of_kernel : num -0.1927 -0.7843 -0.7101 0.0412 -0.5706 ...
## $ width_of_kernel : num 0.182 0.192 0.303 0.785 0.126 ...
## $ asymetry_coefficient : num -1.794 -0.677 -0.97 -1.57 -0.835 ...
## $ length_of_kernel_groove: num -0.941 -1.207 -1.247 -0.498 -0.941 ...
dist_mat<-dist(df_sc, method='euclidean')
hclust_avg<-hclust(dist_mat, method='average')
plot(hclust_avg)
library(dendextend)
## Warning: package 'dendextend' was built under R version 4.3.3
##
## ---------------------
## Welcome to dendextend version 1.17.1
## Type citation('dendextend') for how to cite the package.
##
## Type browseVignettes(package = 'dendextend') for the package vignette.
## The github page is: https://github.com/talgalili/dendextend/
##
## Suggestions and bug-reports can be submitted at: https://github.com/talgalili/dendextend/issues
## You may ask questions at stackoverflow, use the r and dendextend tags:
## https://stackoverflow.com/questions/tagged/dendextend
##
## To suppress this message use: suppressPackageStartupMessages(library(dendextend))
## ---------------------
##
## Attaching package: 'dendextend'
## The following object is masked from 'package:stats':
##
## cutree
avg_dend_obj <- as.dendrogram(hclust_avg)
avg_col_dend <- color_branches(avg_dend_obj, h = 3)
plot(avg_col_dend)
hclust_avg
##
## Call:
## hclust(d = dist_mat, method = "average")
##
## Cluster method : average
## Distance : euclidean
## Number of objects: 202
cut_avg<-cutree(hclust_avg, k=3)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
seeds_df_cl <- mutate(df_sc, cluster = cut_avg)
seeds_df_cl
## area perimeter compactness length_of_kernel width_of_kernel
## 1 -0.008456597 -0.013309465 0.43474889 -0.19272564 0.18170996
## 2 -0.210028049 -0.378886149 1.45640030 -0.78428689 0.19225446
## 3 -0.363768986 -0.493128863 1.05030455 -0.71006057 0.30297173
## 4 0.422018028 0.306570134 1.38800523 0.04119973 0.78538270
## 5 -0.179279861 -0.287491978 1.03320578 -0.57060507 0.12635132
## 6 -0.073369438 -0.074238912 0.38345258 -0.17248210 -0.01336333
## 7 0.589424827 0.664530637 0.16116859 0.92966625 0.52967852
## 8 0.524511987 0.504590838 0.72970264 0.54953753 0.63512354
## 9 0.121369083 0.199943601 -0.05684071 0.16715954 -0.05817746
## 10 -0.298856146 -0.325572882 0.37062850 -0.45364239 -0.16625861
## 11 -0.346686660 -0.432199415 0.72970264 -0.45139311 -0.17153086
## 12 -0.384267778 -0.401734692 0.21246490 -0.36142181 -0.28488426
## 13 -0.397933639 -0.409350873 0.14834451 -0.35467397 -0.39560153
## 14 -0.107534090 -0.234178711 1.21274285 -0.64932995 0.18170996
## 15 -0.312522007 -0.576906853 2.02493436 -1.17116345 0.31351623
## 16 0.268277091 0.123781792 1.49059784 -0.25345626 0.65884867
## 17 -0.069952972 -0.287491978 1.89669359 -0.97772517 0.53231465
## 18 -0.746413098 -0.774927557 -0.09958763 -0.93049024 -0.56694968
## 19 -0.254442097 -0.142784540 -0.53560623 0.04119973 -0.35605964
## 20 -0.271524424 -0.249411073 0.05430129 -0.26920124 -0.25325075
## 21 0.333189931 0.238024506 1.19136939 -0.04877157 0.64039579
## 22 -0.965066876 -1.033877708 -0.19363086 -1.21614910 -0.86483186
## 23 0.035957451 0.131397973 -0.22355370 0.33585571 -0.05026909
## 24 0.439100355 0.436045210 0.59718719 0.43482413 0.41368900
## 25 -0.643919139 -0.630220119 -0.29194878 -0.55036153 -0.62758057
## 26 -0.739580167 -0.698765747 -0.62110007 -0.55036153 -0.81210936
## 27 -0.271524424 -0.310340521 0.47322112 -0.22196631 -0.11353610
## 28 -0.497011132 -0.432199415 -0.45011238 -0.27819837 -0.52477168
## 29 -0.596088626 -0.584523034 -0.20218024 -0.41765387 -0.76202297
## 30 0.199947785 0.268489229 0.06285067 0.26387868 0.28188273
## 31 -0.278357354 -0.135168359 -0.77071429 0.17390738 -0.20580049
## 32 -0.329604334 -0.317956701 0.07994944 -0.12299788 -0.30070101
## 33 0.049623313 0.070468525 0.29795874 0.16266097 0.16852933
## 34 0.442516820 0.519823200 0.10559759 0.41907916 0.52704240
## 35 0.743165765 0.603601190 1.58036638 0.43257485 1.10435388
## 36 -0.035788319 -0.051390369 0.48604519 0.03670116 0.06308431
## 37 -0.213444514 -0.317956701 1.00328294 -0.54586296 0.08944557
## 38 -0.466262945 -0.561674491 0.69123041 -0.65607780 -0.28488426
## 39 -0.479928806 -0.561674491 0.61001126 -0.64932995 -0.27961200
## 40 -0.596088626 -0.790159919 1.28113793 -1.12842709 -0.16625861
## 41 0.203364250 0.207559782 0.47322112 0.53379256 0.34778586
## 42 0.070122104 -0.036158007 1.18282001 -0.13649358 0.52177015
## 43 -0.377434848 -0.416967053 0.36207912 -0.59309789 -0.28752038
## 44 0.155533736 0.131397973 0.64848349 0.13791887 0.33987749
## 45 0.029124521 -0.020925646 0.74252672 -0.15673712 0.29769948
## 46 -0.039204785 -0.051390369 0.46894642 -0.21296918 0.07099269
## 47 -0.015289528 0.062852345 -0.14233455 0.08618537 -0.01599945
## 48 -0.162197535 -0.142784540 0.17826736 -0.12299788 0.02090630
## 49 0.299025278 0.245640687 0.91351440 0.07718824 0.44795863
## 50 -0.141698743 0.017155259 -0.73224206 0.16940882 -0.39823765
## 51 -0.196362188 -0.234178711 0.52024273 -0.30518975 -0.17153086
## 52 -0.131449347 0.009539078 -0.65102291 0.22789016 -0.39823765
## 53 0.042790382 0.139014154 -0.21927901 0.14016815 -0.13726123
## 54 -0.151948139 -0.180865445 0.46467173 -0.56610650 0.29769948
## 55 0.005209264 -0.119935998 1.26831385 -0.57510363 0.38996387
## 56 0.162366667 0.139014154 0.63138472 0.05019686 0.40841675
## 57 -0.954817480 -0.851089366 -1.35634711 -1.08119216 -0.61176382
## 58 -1.255466425 -1.490848563 0.55871496 -1.65925772 -1.01509102
## 59 -0.869405848 -1.064342432 0.91351440 -1.26788259 -0.58540256
## 60 -0.575589834 -0.569290672 -0.12523578 -0.55036153 -0.51159105
## 61 -0.725914306 -0.774927557 0.02865313 -0.84951608 -0.62758057
## 62 -0.691749653 -0.828240823 0.72542795 -1.12617781 -0.38242090
## 63 -0.192945722 -0.165633083 0.07140006 -0.02178018 -0.19525599
## 64 -0.305689077 -0.226562530 -0.36034385 -0.06901511 -0.27961200
## 65 -0.182696327 -0.150400721 0.07140006 -0.15898640 -0.29279263
## 66 -0.742996633 -0.637836300 -1.07421743 -0.51212373 -1.00718264
## 67 0.931071355 1.060572045 -0.15515863 1.24006721 0.78274657
## 68 0.661170598 0.824470437 -0.36889323 0.80595572 0.57976491
## 69 0.804662140 0.870167522 0.22956366 0.76097007 0.86973871
## 70 1.436708217 1.273825111 1.58891576 1.15684377 1.75547688
## 71 0.654337667 0.702611542 0.32788158 0.84869208 0.58503716
## 72 0.637255341 0.786389532 -0.30477285 0.64625667 0.45850313
## 73 0.825160931 1.007258779 -0.47148584 0.95440835 0.36623874
## 74 1.983342662 2.012594660 0.22956366 2.11278876 1.44968632
## 75 1.378628307 1.448997272 0.17399267 1.81138493 0.98836436
## 76 0.756831626 0.733076265 0.78099895 0.47306193 0.79592720
## 77 0.555260174 0.573136466 0.48604519 0.52929399 0.53495077
## 78 1.303466071 1.220511844 1.14434778 0.82394998 1.56303972
## 79 1.809102932 1.753644509 0.78954833 1.45149975 1.58149260
## 80 1.593865620 1.639401795 0.29795874 1.67417870 1.33896905
## 81 1.573366828 1.616553252 0.30223343 1.63369162 1.41541669
## 82 1.149725133 1.144350035 0.68695572 1.19958013 1.01999787
## 83 1.358129515 1.273825111 1.11015024 0.99939400 1.31788005
## 84 1.392294168 1.578472348 -0.50995807 2.04531029 1.07008425
## 85 2.143916530 1.997362298 1.19564408 2.09929306 2.02699781
## 86 2.041422571 1.875503404 1.37518115 1.82263134 2.02436168
## 87 1.774938279 1.829806318 0.15689390 2.11728732 1.37323868
## 88 1.317131932 1.228128025 1.17427062 1.19733085 1.40223606
## 89 1.334214258 1.296673654 0.84084464 1.42225908 1.13071514
## 90 1.259052022 1.113885311 1.52479538 0.89367773 1.57094809
## 91 1.180473321 1.471845815 -1.09986559 2.30847632 0.58240103
## 92 0.671419994 0.809238075 -0.26202593 1.12310453 0.52440627
## 93 1.505037522 1.525159081 0.45184765 1.57745956 1.43914182
## 94 1.392294168 1.509926719 -0.09531294 1.82038205 0.75902144
## 95 1.115560480 1.273825111 -0.30904754 1.42000980 0.65357642
## 96 1.303466071 1.334754558 0.43047419 1.30304712 1.10699001
## 97 0.514262591 0.504590838 0.66985695 0.17615667 0.68784605
## 98 1.054064105 0.969177874 1.20846816 0.56303323 1.13335126
## 99 1.556284501 1.456613453 1.17854532 1.06462319 1.65530411
## 100 1.460623474 1.555623805 0.03292783 1.64043947 1.09908163
## 101 1.382044772 1.395684006 0.51169334 1.36827630 1.29415492
## 102 1.341047189 1.296673654 0.88786625 0.89367773 1.37587481
## 103 1.347880119 1.205279483 1.48204846 1.15234520 1.42859732
## 104 0.931071355 0.969177874 0.38772727 0.88468060 0.81438008
## 105 1.720274835 1.776493052 0.18254205 2.32871986 1.31524392
## 106 1.245386161 1.243360387 0.66558226 1.15459448 1.08062875
## 107 1.211221508 1.167198578 0.90496502 1.05112750 1.33106067
## 108 1.528952779 1.624169433 0.02865313 1.49198683 1.38905544
## 109 1.443541147 1.311906015 1.39227992 1.22207295 1.68166537
## 110 1.446957612 1.540391443 0.05430129 1.39301841 1.24670466
## 111 2.072170759 2.027827022 0.63993411 2.07680024 1.91628053
## 112 1.419625890 1.418532548 0.61856065 1.74615574 1.19925440
## 113 1.385461237 1.228128025 1.57181699 0.92516768 1.66848474
## 114 1.450374078 1.418532548 0.77244956 1.36152846 1.45232245
## 115 1.361545980 1.250976568 1.27686323 1.32104138 1.33106067
## 116 1.751023022 1.761260690 0.43474889 1.91935048 1.56303972
## 117 1.822768793 1.768876871 0.80237241 1.51897822 1.83983290
## 118 1.105311084 1.167198578 0.26803589 0.94316194 0.78801882
## 119 0.432267424 0.603601190 -0.51850746 0.27512509 0.32406073
## 120 1.204388578 1.052955864 1.57181699 0.76546864 1.33633293
## 121 0.370771049 0.230408325 1.51624599 -0.62233856 0.83810521
## 122 1.313715467 1.212895663 1.23839101 1.06012462 1.59467322
## 123 1.279550814 1.388067825 -0.04829132 1.45149975 0.86973871
## 124 1.050647640 0.961561693 1.21274285 0.76321936 1.11489838
## 125 1.795437071 1.860271042 0.10987228 1.96433612 1.34160518
## 126 0.903739633 0.816854256 1.20419347 0.34035428 1.12280676
## 127 1.159974529 0.992026417 1.70433245 0.76321936 1.29415492
## 128 1.378628307 1.319522196 0.99473355 1.13435094 1.47868370
## 129 0.162366667 0.238024506 -0.01409379 0.54953753 0.01036180
## 130 0.428850959 0.565520285 -0.27912470 0.46181552 0.34514974
## 131 0.223863042 0.230408325 0.48604519 0.30661504 0.37941937
## 132 0.838826792 0.893016065 0.32360689 1.13660022 0.81701620
## 133 0.227279507 0.428429029 -0.77926368 0.63051170 -0.08717484
## 134 0.237528903 0.397964305 -0.55270499 0.43257485 0.05781206
## 135 0.452766216 0.451277571 0.60146188 0.52254615 0.54813140
## 136 -0.626836813 -0.508361224 -0.98017421 -0.37716679 -0.71193659
## 137 -0.541425181 -0.493128863 -0.41164015 -0.22196631 -0.50368267
## 138 -0.534592251 -0.485512682 -0.38171731 -0.56385722 -0.50104655
## 139 -0.917236362 -0.965332080 -0.24492716 -0.93498881 -0.78311197
## 140 -1.053894973 -0.904402632 -1.86076079 -0.73255340 -1.28397582
## 141 -1.262299355 -1.110039517 -2.31815284 -0.81127828 -1.52122711
## 142 -1.187137119 -1.110039517 -1.60000457 -1.04295436 -1.43687110
## 143 -0.824991799 -0.858705547 -0.21927901 -0.83826967 -0.78311197
## 144 -0.753246028 -0.668301024 -0.93315259 -0.57060507 -0.93073500
## 145 -1.405790897 -1.262363136 -2.57463437 -0.72580555 -1.62403601
## 146 -1.050478508 -1.033877708 -0.91177913 -0.84726680 -1.11789991
## 147 -0.988982133 -0.813008461 -1.96762809 -0.52786871 -1.28661194
## 148 -0.903570501 -0.752079014 -1.60855395 -0.52112086 -1.13635279
## 149 -1.272548751 -1.178585145 -1.89495832 -0.94398594 -1.50541036
## 150 -1.211052376 -1.170968965 -1.39909404 -1.04520364 -1.34197058
## 151 -1.269132286 -1.170968965 -1.95052932 -0.87650747 -1.55286062
## 152 -1.217885307 -1.308060221 -0.48430992 -1.31961609 -1.09417478
## 153 -0.947984550 -0.653068662 -2.68577637 -0.55261081 -1.36833184
## 154 -1.077810230 -0.813008461 -2.68150167 -0.44014669 -1.54495224
## 155 -1.166638327 -1.041493889 -1.90778240 -0.75504622 -1.50013811
## 156 -0.807909473 -0.698765747 -1.21528227 -0.42440172 -1.01509102
## 157 -0.985565668 -0.957715899 -0.88185629 -0.65157923 -1.19698368
## 158 -0.975316272 -0.896786451 -1.25375450 -0.83826967 -1.09944703
## 159 -0.804493008 -0.774927557 -0.64674822 -0.68981703 -0.78047585
## 160 -1.286214612 -1.368989668 -0.64674822 -1.41408595 -1.23916169
## 161 -0.958233945 -1.094807155 0.35780443 -1.20265341 -0.85165124
## 162 -0.842074126 -0.759695195 -1.05711867 -0.72130699 -0.96764076
## 163 -0.941151619 -0.866321728 -1.13833782 -0.50087732 -1.12580829
## 164 -1.214468841 -1.117655698 -1.78809102 -1.04295436 -1.57131350
## 165 -1.146139536 -1.132888060 -1.08704151 -1.06319790 -1.10471929
## 166 -1.241800564 -1.231898412 -1.24093043 -1.24089121 -1.32088158
## 167 -1.197386515 -1.148120422 -1.42901688 -1.13292565 -1.32088158
## 168 -1.392125036 -1.239514593 -2.60883191 -0.81352756 -1.64248889
## 169 -1.402374432 -1.536545649 -0.50995807 -1.48156442 -1.16798630
## 170 -1.245217029 -1.201433688 -1.51451072 -1.02046154 -1.46059623
## 171 -1.422873224 -1.414686754 -1.62565272 -1.11268211 -1.63985276
## 172 -1.170054793 -1.170968965 -1.01009705 -1.03395723 -1.33406220
## 173 -0.920652827 -0.851089366 -1.09559089 -0.63583426 -0.97818526
## 174 -1.193970050 -1.247130774 -0.63819884 -1.23639264 -1.28924807
## 175 -0.835241195 -0.896786451 -0.01409379 -0.90799742 -0.65130570
## 176 -0.927485758 -0.934867356 -0.55697969 -0.89900029 -0.93600725
## 177 -1.111974883 -1.155736603 -0.57407846 -1.19590556 -1.09153866
## 178 -0.688333188 -0.622603938 -0.71941799 -0.32543329 -0.62758057
## 179 -1.142723071 -0.972948261 -2.18563739 -0.62233856 -1.53177162
## 180 -1.057311439 -0.866321728 -2.18563739 -0.50987445 -1.44477947
## 181 -1.364793314 -1.361373488 -1.44184096 -1.24089121 -1.55286062
## 182 -1.255466425 -1.346141126 -0.49285930 -1.23864192 -1.16798630
## 183 -1.474120203 -1.658404543 -0.26202593 -1.66600557 -1.25761456
## 184 -1.357960383 -1.361373488 -1.36489650 -1.33536106 -1.44214335
## 185 -1.241800564 -1.315676402 -0.62537476 -1.23414336 -1.21280043
## 186 -1.036812647 -1.193817507 0.36635381 -1.14192278 -0.82001773
## 187 -1.395541501 -1.338524945 -1.93770524 -1.03395723 -1.67148627
## 188 -0.954817480 -1.003412984 -0.30049816 -0.90799742 -0.76202297
## 189 -0.719081376 -0.851089366 0.64420880 -1.07894288 -0.36396802
## 190 -0.722497841 -0.805392280 0.32788158 -0.93498881 -0.55376906
## 191 -0.524342855 -0.614987757 0.59718719 -0.71905770 -0.35869577
## 192 -0.780577751 -0.698765747 -0.97589952 -0.51662229 -0.93073500
## 193 -0.732747237 -0.919634994 1.08877678 -1.27463044 -0.28752038
## 194 -0.862572917 -0.873937909 -0.42873892 -0.94623522 -0.72511721
## 195 -1.272548751 -1.422302935 -0.12523578 -1.41858451 -1.19698368
## 196 -0.753246028 -0.896786451 0.70405449 -1.02720938 -0.45623241
## 197 -0.865989383 -0.851089366 -0.60827599 -0.97997445 -0.80156485
## 198 -0.927485758 -1.056726251 0.31505751 -1.13067637 -0.74620622
## 199 -1.255466425 -1.300444040 -0.84765875 -1.12392852 -1.23652556
## 200 -0.582422764 -0.706381928 0.74252672 -0.90799742 -0.08453872
## 201 -1.047062043 -1.049110070 -0.80491183 -1.04520364 -1.12844441
## 202 -0.889904640 -0.950099718 -0.10813701 -0.89225244 -0.76465910
## asymetry_coefficient length_of_kernel_groove cluster
## 1 -1.79447044 -0.94122576 1
## 2 -0.67716286 -1.20653517 1
## 3 -0.96961695 -1.24704042 1
## 4 -1.57047719 -0.49769325 1
## 5 -0.83468927 -0.94122576 1
## 6 -0.08760198 -0.40858169 1
## 7 -1.11517933 0.92404111 2
## 8 -1.16237079 0.22735077 2
## 9 0.54848568 -0.21618175 3
## 10 -1.32986723 -0.85008895 1
## 11 0.17826538 -1.38273302 3
## 12 -0.38670276 -1.11134782 3
## 13 -0.52229512 -1.20653517 3
## 14 0.31053440 -1.29564672 3
## 15 1.00777155 -1.29564672 3
## 16 -1.40829810 -0.75895213 1
## 17 -1.29663381 -1.56298139 1
## 18 0.25536692 -1.02628679 3
## 19 -0.42924154 -0.49566799 1
## 20 -0.68447421 -0.40858169 1
## 21 -1.96256508 -0.66781531 1
## 22 -1.53059709 -0.93109945 1
## 23 -1.28068177 -0.85008895 1
## 24 -1.87090731 -0.23035858 1
## 25 -0.22917635 -1.20653517 3
## 26 -0.80677319 -1.11742361 3
## 27 -0.64060609 -0.77515423 1
## 28 -0.12415874 -0.65566373 3
## 29 -1.90274493 -0.73869950 1
## 30 -0.20325428 -0.39035433 2
## 31 0.13439727 -0.24656069 3
## 32 -1.05934719 -0.82781106 1
## 33 -1.05602385 -0.12301967 1
## 34 -0.59474397 0.21519919 2
## 35 -0.50634307 0.12811290 1
## 36 -0.40265480 -0.22630806 1
## 37 1.97220541 -0.85008895 3
## 38 -0.75160572 -0.49161746 1
## 39 -0.97626364 -0.49566799 1
## 40 -0.83535394 -1.29159620 1
## 41 0.66014998 0.21722446 2
## 42 -0.39202011 -0.48756694 1
## 43 -1.43422017 -0.93109945 1
## 44 -1.56250117 -0.58477954 1
## 45 -0.50501374 -0.49769325 1
## 46 -0.67383951 -0.62731006 1
## 47 -1.05602385 -0.14124703 1
## 48 0.17095403 -0.56047639 1
## 49 1.24638751 -0.57667849 3
## 50 0.26467228 -0.05011021 3
## 51 -0.25908643 -0.39845538 1
## 52 -1.48672897 0.13418869 1
## 53 -1.18629885 0.03697608 1
## 54 -0.60870201 -0.76300265 1
## 55 -1.71205156 -0.67389110 1
## 56 -1.14243074 -0.40250591 1
## 57 -1.47277094 -1.82626553 1
## 58 -0.96297027 -1.45361721 1
## 59 -0.33087062 -1.65209295 1
## 60 0.29192368 -0.67389110 3
## 61 -1.68945284 -1.29362146 1
## 62 -0.90780279 -1.64804242 1
## 63 -1.59839326 -0.54832482 1
## 64 -0.99753303 -0.58477954 1
## 65 -1.49802834 -0.24453542 1
## 66 -0.12282941 -0.71642161 3
## 67 0.23808554 1.29466417 2
## 68 0.63622191 0.92404111 2
## 69 0.54582701 0.74986852 2
## 70 -0.51963644 1.33314416 2
## 71 0.19022941 0.85113166 2
## 72 0.79906567 0.75796958 2
## 73 0.07058910 1.01517793 2
## 74 0.48733619 2.08654186 2
## 75 0.89477792 1.90629348 2
## 76 -0.57148058 0.65873171 2
## 77 1.20584274 0.93011690 2
## 78 1.06759171 0.92809164 2
## 79 0.96722678 1.55187252 2
## 80 -1.49271099 1.72604511 2
## 81 -0.50235506 1.54782200 2
## 82 -0.84731797 1.57212515 2
## 83 -1.37506468 1.39390204 2
## 84 -0.01781180 2.18172920 2
## 85 1.37068050 1.64098408 2
## 86 0.86287384 1.82325771 2
## 87 -1.17167615 2.08249133 2
## 88 -0.39733746 1.28048733 2
## 89 -0.31957125 1.28048733 2
## 90 1.51757222 0.92404111 2
## 91 0.80770636 2.08046607 2
## 92 -0.01448846 1.10631475 2
## 93 -0.16005084 1.65516092 2
## 94 -1.04605382 2.09059238 2
## 95 -0.57480392 1.72604511 2
## 96 -1.01680841 1.36959889 2
## 97 0.33180378 0.39949809 2
## 98 -1.09656862 0.84303061 2
## 99 0.39228861 1.19137578 2
## 100 -0.23981104 1.63693355 2
## 101 -0.23249969 1.47288728 2
## 102 -0.77420444 0.92809164 2
## 103 -0.58145060 1.57820094 2
## 104 0.01940963 1.02935477 2
## 105 -0.30960123 2.28704286 2
## 106 -1.31590919 0.95847058 2
## 107 -0.98556900 0.75594431 2
## 108 -0.02645249 1.10226422 2
## 109 -1.06931721 1.01922845 2
## 110 1.97021140 1.28048733 2
## 111 0.63755125 1.81313140 2
## 112 -0.97692831 1.50326622 2
## 113 0.40956999 0.66683276 2
## 114 -0.42126552 1.54782200 2
## 115 -0.05237455 1.10428948 2
## 116 -0.43522356 1.82123245 2
## 117 1.45110538 1.55389779 2
## 118 -0.06566792 1.19542630 2
## 119 0.37766590 0.57164541 2
## 120 -0.48773236 0.98074846 2
## 121 -0.25376908 -0.56047639 1
## 122 0.31252840 1.15694631 2
## 123 0.44745609 1.37972520 2
## 124 -0.97094629 1.00910214 2
## 125 -1.20158623 1.54782200 2
## 126 1.09550778 0.48658438 2
## 127 -0.58543862 1.09618843 2
## 128 -0.53824716 1.06986002 2
## 129 0.49464754 0.75796958 2
## 130 0.36437254 0.75796958 2
## 131 0.83362843 0.86328323 2
## 132 -0.12748208 1.11441580 2
## 133 -0.71637829 0.92809164 2
## 134 -0.65988148 0.67088328 2
## 135 0.03403234 1.01517793 2
## 136 1.05429834 -0.05213547 3
## 137 2.20483935 0.03900134 3
## 138 1.51358421 -0.23035858 3
## 139 1.16396863 -0.40453117 3
## 140 0.50062956 -0.49161746 3
## 141 1.62923651 -0.29516699 3
## 142 -0.99487435 -0.58477954 3
## 143 0.46739614 -0.84806368 3
## 144 -0.30428388 -0.21213122 3
## 145 1.15931595 -0.45921326 3
## 146 0.98184948 -0.23035858 3
## 147 2.17625861 -0.30529330 3
## 148 0.69006005 -0.12301967 3
## 149 -0.25642775 -0.85008895 3
## 150 0.21947482 -0.31947014 3
## 151 1.39261456 -0.40858169 3
## 152 -0.24645773 -0.84603842 3
## 153 0.73592217 -0.40655643 3
## 154 0.43881540 -0.22428280 3
## 155 1.11013049 -0.22428280 3
## 156 -0.42259486 0.14228974 3
## 157 0.36769588 -0.22833332 3
## 158 0.84426312 -0.75895213 3
## 159 0.46606680 -0.49566799 3
## 160 1.77479889 -0.75287634 3
## 161 -1.00816772 -0.73869950 1
## 162 0.80172435 -0.30529330 3
## 163 -0.05303922 -0.16757544 3
## 164 0.41156399 -0.58477954 3
## 165 1.99214546 -0.94122576 3
## 166 0.39295328 -0.85211421 3
## 167 1.24306417 -0.67186584 3
## 168 0.97320880 -0.47744062 3
## 169 0.70135942 -0.72452266 3
## 170 1.07490306 -0.66579005 3
## 171 0.65416796 -0.92704893 3
## 172 1.43448867 -0.84806368 3
## 173 -1.36708866 -0.49161746 3
## 174 0.82365840 -1.20653517 3
## 175 0.84359846 -0.55440060 3
## 176 0.75719156 -0.53212271 3
## 177 0.99115484 -0.57870376 3
## 178 1.63987121 -0.21213122 3
## 179 0.22878018 -0.48351641 3
## 180 0.78444297 -0.13922177 3
## 181 0.30654638 -0.94122576 3
## 182 2.52986220 -0.93920050 3
## 183 0.83562244 -1.26931831 3
## 184 1.11677717 -0.76097739 3
## 185 0.17760071 -0.85008895 3
## 186 -0.08029063 -0.58477954 3
## 187 0.75453289 -0.67186584 3
## 188 0.27530697 -0.82781106 3
## 189 0.76782626 -1.02628679 3
## 190 1.17327399 -0.93717524 3
## 191 0.63289857 -0.66781531 3
## 192 -0.27370913 -0.38427854 3
## 193 -0.59142063 -1.19640886 1
## 194 1.16596263 -0.76097739 3
## 195 0.22146883 -1.20045938 3
## 196 3.14933315 -0.85211421 3
## 197 0.13373260 -0.85008895 3
## 198 -0.05769190 -1.11539835 3
## 199 0.40358797 -0.84603842 3
## 200 3.05561491 -0.73869950 3
## 201 -0.07962596 -0.76300265 3
## 202 1.27563292 -0.72452266 3
count(seeds_df_cl,cluster)
## cluster n
## 1 1 48
## 2 2 73
## 3 3 81
library(ggplot2)
ggplot(seeds_df_cl, aes(x=area, y = perimeter, color = factor(cluster))) +geom_point()
library(clValid)
## Warning: package 'clValid' was built under R version 4.3.3
## Loading required package: cluster
dunn(dist_mat,cut_avg)
## [1] 0.1056525
#This is an algorithm for the k-means clustering applied to the iris dataset
set.seed(1234) #setting a seed for the random number generator
data(iris)
ir3<-kmeans(iris[,-5],center=3, iter.max=200) #not using species info
ir3
## K-means clustering with 3 clusters of sizes 50, 62, 38
##
## Cluster means:
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 5.006000 3.428000 1.462000 0.246000
## 2 5.901613 2.748387 4.393548 1.433871
## 3 6.850000 3.073684 5.742105 2.071053
##
## 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 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [75] 2 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 3 3 3 3 2 3 3 3 3
## [112] 3 3 2 2 3 3 3 3 2 3 2 3 2 3 3 2 2 3 3 3 3 3 2 3 3 3 3 2 3 3 3 2 3 3 3 2 3
## [149] 3 2
##
## Within cluster sum of squares by cluster:
## [1] 15.15100 39.82097 23.87947
## (between_SS / total_SS = 88.4 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"
ir3$betweenss
## [1] 602.5192
table(ir3$cluster, iris$Species)
##
## setosa versicolor virginica
## 1 50 0 0
## 2 0 48 14
## 3 0 2 36
cm<- table(ir3$cluster, iris$Species)
1-sum(diag(cm))/sum(cm)
## [1] 0.1066667