CS583_Classwork_2-27
Destinee Redfearn
2024-02-27
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library(readr)
seeds_dataset <- read_delim("C:/Users/dnred/Downloads/seeds_dataset.txt",
delim = "\t", escape_double = FALSE,
col_names = 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: 210 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (1): X8
## dbl (7): X1, X2, X3, X4, X5, X6, X7
##
## 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.sd <- seeds_dataset
str(sd)## spc_tbl_ [210 × 8] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ X1: num [1:210] 15.3 14.9 14.3 13.8 16.1 ...
## $ X2: num [1:210] 14.8 14.6 14.1 13.9 15 ...
## $ X3: num [1:210] 0.871 0.881 0.905 0.895 0.903 ...
## $ X4: num [1:210] 5.76 5.55 5.29 5.32 5.66 ...
## $ X5: num [1:210] 3.31 3.33 3.34 3.38 3.56 ...
## $ X6: num [1:210] 2.22 1.02 2.7 2.26 1.35 ...
## $ X7: num [1:210] 5.22 4.96 4.83 4.8 5.17 ...
## $ X8: chr [1:210] "1" "1" "1" "1" ...
## - attr(*, "spec")=
## .. cols(
## .. X1 = col_double(),
## .. X2 = col_double(),
## .. X3 = col_double(),
## .. X4 = col_double(),
## .. X5 = col_double(),
## .. X6 = col_double(),
## .. X7 = col_double(),
## .. X8 = 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.:12.27 1st Qu.:13.45 1st Qu.:0.8577 1st Qu.:5.2447
## Median :14.36 Median :14.32 Median :0.8735 Median :5.5180
## Mean :14.85 Mean :14.56 Mean :0.8713 Mean :5.5639
## 3rd Qu.:17.30 3rd Qu.:15.71 3rd Qu.:0.8877 3rd Qu.:5.9798
## 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.:2.956 1st Qu.:2.6002 1st Qu.:5.045
## Median :3.245 Median :3.5990 Median :5.226
## Mean :3.281 Mean :3.6935 Mean :5.408
## 3rd Qu.:3.566 3rd Qu.:4.7687 3rd Qu.:5.879
## Max. :5.325 Max. :8.4560 Max. :6.735
## NA's :1 NA's :4
## type_of_seed
## Length:210
## Class :character
## Mode :character
##
##
##
## any(is.na(sd))## [1] TRUEsd<-na.omit(sd)
str(sd)## tibble [203 × 8] (S3: tbl_df/tbl/data.frame)
## $ area : num [1:203] 15.3 14.9 14.3 13.8 16.1 ...
## $ perimeter : num [1:203] 14.8 14.6 14.1 13.9 15 ...
## $ compactness : num [1:203] 0.871 0.881 0.905 0.895 0.903 ...
## $ length_of_kernel : num [1:203] 5.76 5.55 5.29 5.32 5.66 ...
## $ width_of_kernel : num [1:203] 3.31 3.33 3.34 3.38 3.56 ...
## $ asymetry_coefficient : num [1:203] 2.22 1.02 2.7 2.26 1.35 ...
## $ length_of_kernel_groove: num [1:203] 5.22 4.96 4.83 4.8 5.17 ...
## $ type_of_seed : chr [1:203] "1" "1" "1" "1" ...
## - attr(*, "na.action")= 'omit' Named int [1:7] 8 36 61 136 170 171 202
## ..- attr(*, "names")= chr [1:7] "8" "36" "61" "136" ...sd<-sd[,-8]
View(sd)
df_sc<-as.data.frame(scale(sd))
str(df_sc)## 'data.frame': 203 obs. of 7 variables:
## $ area : num 0.12107 -0.00908 -0.21114 -0.36526 0.42245 ...
## $ perimeter : num 0.1918 -0.0143 -0.3807 -0.4953 0.3064 ...
## $ compactness : num 0.003 0.436 1.46 1.053 1.391 ...
## $ length_of_kernel : num 0.2766 -0.1945 -0.7875 -0.7131 0.0399 ...
## $ width_of_kernel : num 0.126 0.182 0.192 0.303 0.787 ...
## $ asymetry_coefficient : num -0.99 -1.79 -0.672 -0.965 -1.566 ...
## $ length_of_kernel_groove: num -0.405 -0.941 -1.207 -1.248 -0.497 ...dist_mat<-dist(df_sc, method='euclidean')
hclust_avg<-hclust(dist_mat, method='average')
plot(hclust_avg)
library(dendextend)##
## ---------------------
## 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: 203cut_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, unionseeds_df_cl <- mutate(df_sc, cluster = cut_avg)
seeds_df_cl## area perimeter compactness length_of_kernel width_of_kernel
## 1 0.121066845 0.19183791 0.002997608 0.27664105 0.126036292
## 2 -0.009076639 -0.01429100 0.435814160 -0.19453719 0.181530271
## 3 -0.211141523 -0.38074240 1.460003823 -0.78745525 0.192100553
## 4 -0.365258806 -0.49525846 1.052899145 -0.71305869 0.303088513
## 5 0.422451756 0.30635397 1.391438825 0.03992471 0.786678907
## 6 -0.180318066 -0.28912955 1.035757896 -0.57328333 0.126036292
## 7 -0.074148381 -0.07536624 0.384390411 -0.17424721 -0.014019943
## 8 0.590268354 0.66517097 0.161554166 0.93042904 0.530349572
## 9 0.525196612 0.50484848 0.731500716 0.54942845 0.636052391
## 10 0.121066845 0.19947231 -0.056996766 0.16617343 -0.058943641
## 11 -0.300187064 -0.32730157 0.371534474 -0.45605238 -0.167289030
## 12 -0.348134664 -0.43418323 0.731500716 -0.45379794 -0.172574171
## 13 -0.385807778 -0.40364561 0.212977915 -0.36362029 -0.286204700
## 14 -0.399507092 -0.41128002 0.148698229 -0.35685696 -0.397192660
## 15 -0.108396667 -0.23568872 1.215741017 -0.65218877 0.181530271
## 16 -0.313886378 -0.57923691 2.029950372 -1.17521916 0.313658795
## 17 0.268334472 0.12312827 1.494286323 -0.25540710 0.659835525
## 18 -0.070723553 -0.28912955 1.901391000 -0.98133721 0.532992143
## 19 -0.748839602 -0.77773142 -0.099849890 -0.93399394 -0.568959740
## 20 -0.255664293 -0.14407587 -0.536951755 0.03992471 -0.357554103
## 21 -0.272788436 -0.25095753 0.054421356 -0.27118819 -0.254493855
## 22 0.333406214 0.23764433 1.194314455 -0.05025294 0.641337532
## 23 -0.968028628 -1.03730116 -0.194126763 -1.22030799 -0.867570202
## 24 0.035446132 0.13076268 -0.224123949 0.33525653 -0.051015929
## 25 0.439575898 0.43613884 0.598656031 0.43445194 0.414076472
## 26 -0.646094746 -0.63267774 -0.292688948 -0.55299336 -0.629738861
## 27 -0.741989945 -0.70138738 -0.622658003 -0.55299336 -0.814718793
## 28 -0.272788436 -0.31203276 0.474381972 -0.22384492 -0.114437620
## 29 -0.498827119 -0.43418323 -0.451245507 -0.28020596 -0.526678612
## 30 -0.598147147 -0.58687131 -0.202697387 -0.41998132 -0.764509954
## 31 0.199837901 0.26818195 0.062991981 0.26311440 0.281947949
## 32 -0.279638093 -0.13644147 -0.772643937 0.17293675 -0.206927587
## 33 -0.331010521 -0.31966717 0.080133231 -0.12464950 -0.302060123
## 34 0.049145446 0.06968744 0.298684163 0.16166454 0.168317419
## 35 0.443000727 0.52011729 0.105845105 0.41867085 0.527707002
## 36 0.744385638 0.60409573 1.584277883 0.43219750 1.106429933
## 37 -0.036475267 -0.05246302 0.487237909 0.03541583 0.062614601
## 38 -0.214566351 -0.31966717 1.005760709 -0.54848447 0.089040305
## 39 -0.468003662 -0.56396810 0.692932904 -0.65895210 -0.286204700
## 40 -0.481702976 -0.56396810 0.611511968 -0.65218877 -0.280919559
## 41 -0.598147147 -0.79300023 1.284306015 -1.13238478 -0.167289030
## 42 0.203262730 0.20710672 0.474381972 0.53364736 0.348012211
## 43 0.069694417 -0.03719422 1.185743830 -0.13817615 0.522421861
## 44 -0.378958121 -0.41891442 0.362963849 -0.59582774 -0.288847271
## 45 0.155315130 0.13076268 0.650079780 0.13686569 0.340084499
## 46 0.028596475 -0.02192541 0.744356653 -0.15846612 0.297803372
## 47 -0.039900096 -0.05246302 0.470096659 -0.21482716 0.070542312
## 48 -0.015926296 0.06205304 -0.142703014 0.08501354 -0.016662513
## 49 -0.163193923 -0.14407587 0.178695416 -0.12464950 0.020333473
## 50 0.299157929 0.24527874 0.915769149 0.07599577 0.448429888
## 51 -0.142644952 0.01624661 -0.734076125 0.16842787 -0.399835230
## 52 -0.197442208 -0.23568872 0.521520408 -0.30725925 -0.172574171
## 53 -0.132370466 0.00861221 -0.652655189 0.22704334 -0.399835230
## 54 0.042295789 0.13839708 -0.219838637 0.13912013 -0.138220755
## 55 -0.152919437 -0.18224789 0.465811347 -0.56877445 0.297803372
## 56 0.004622675 -0.12117266 1.271450078 -0.57779221 0.390293338
## 57 0.162164787 0.13839708 0.632938530 0.04894248 0.408791331
## 58 -0.957754142 -0.85407546 -1.359731735 -1.08504151 -0.613883438
## 59 -1.259139053 -1.49536541 0.560088220 -1.66443293 -1.018196718
## 60 -0.872133429 -1.06783878 0.915769149 -1.27216014 -0.587457733
## 61 -0.577598175 -0.57160251 -0.125561764 -0.55299336 -0.513465760
## 62 -0.728290631 -0.77773142 0.028709482 -0.85283405 -0.629738861
## 63 -0.694042345 -0.83117225 0.727215403 -1.13013034 -0.383979808
## 64 -0.194017380 -0.16697909 0.071562606 -0.02319964 -0.196357305
## 65 -0.307036721 -0.22805432 -0.361253946 -0.07054291 -0.280919559
## 66 -0.183742894 -0.15171028 0.071562606 -0.16072057 -0.294132412
## 67 -0.745414773 -0.64031214 -1.076901117 -0.51466785 -1.010269007
## 68 0.932751207 1.06215998 -0.155558951 1.24154194 0.784036337
## 69 0.662189753 0.82549345 -0.369824571 0.80643476 0.580558411
## 70 0.806032551 0.87129988 0.230119165 0.76134594 0.871241162
## 71 1.439625830 1.27592330 1.592848508 1.15812761 1.759144837
## 72 0.655340096 0.70334299 0.328681350 0.84926915 0.585843552
## 73 0.638215953 0.78732143 -0.305544885 0.64636943 0.459000170
## 74 0.826581522 1.00871915 -0.472672069 0.95522789 0.366510204
## 75 1.987598395 2.01646050 0.230119165 2.11626517 1.452606664
## 76 1.381403745 1.45151459 0.174410104 1.81417004 0.990156833
## 77 0.758084952 0.73388060 0.782924464 0.47277745 0.797249189
## 78 0.556020069 0.57355812 0.487237909 0.52913848 0.535634713
## 79 1.306057517 1.22248247 1.147176018 0.82447029 1.566237194
## 80 1.812932140 1.75689076 0.791495089 1.45345942 1.584735187
## 81 1.597167942 1.64237470 0.298684163 1.67664911 1.341618704
## 82 1.576618971 1.61947149 0.302969476 1.63606917 1.418253248
## 83 1.151940233 1.14613843 0.688647592 1.20096200 1.021867678
## 84 1.360854773 1.27592330 1.112893519 1.00031672 1.320478141
## 85 1.395103059 1.58129947 -0.511239880 2.04863193 1.072076517
## 86 2.148565336 2.00119169 1.198599767 2.10273853 2.031329595
## 87 2.045820480 1.87904123 1.378582888 1.82544224 2.028687025
## 88 1.778683854 1.83323480 0.157268854 2.12077406 1.375972120
## 89 1.319756831 1.23011687 1.177173205 1.19870755 1.405040395
## 90 1.336880974 1.29882651 0.842918838 1.42415169 1.132855638
## 91 1.261534746 1.11560081 1.528568822 0.89435797 1.574164905
## 92 1.182763690 1.47441781 -1.102612991 2.31240157 0.583200982
## 93 0.672464239 0.81022465 -0.262691761 1.12431099 0.525064432
## 94 1.508122400 1.52785864 0.452955410 1.57970814 1.442036382
## 95 1.395103059 1.51258983 -0.095564577 1.82318780 0.760253203
## 96 1.117691948 1.27592330 -0.309830197 1.42189724 0.654550384
## 97 1.306057517 1.33699853 0.431528848 1.30466630 1.109072504
## 98 0.514922126 0.50484848 0.671506342 0.17519119 0.688903800
## 99 1.056045034 0.97054713 1.211455704 0.56295510 1.135498208
## 100 1.559494828 1.45914900 1.181458517 1.06569552 1.658727160
## 101 1.463599629 1.55839625 0.032994794 1.64283249 1.101144792
## 102 1.384828573 1.39807377 0.512949783 1.37004509 1.296695006
## 103 1.343730631 1.29882651 0.890057274 0.89435797 1.378614691
## 104 1.350580288 1.20721366 1.485715698 1.15361873 1.431466100
## 105 0.932751207 0.97054713 0.388675724 0.88534021 0.815747182
## 106 1.723886598 1.77979397 0.182980728 2.33269154 1.317835570
## 107 1.247835432 1.24538568 0.667221030 1.15587317 1.082646799
## 108 1.213587147 1.16904164 0.907198524 1.05216887 1.333690993
## 109 1.532096200 1.62710589 0.028709482 1.49403937 1.391827543
## 110 1.446475487 1.31409532 1.395724137 1.22350641 1.685152864
## 111 1.449900315 1.54312744 0.054421356 1.39484395 1.249128738
## 112 2.076643936 2.03172931 0.641509155 2.08019411 1.920341636
## 113 1.422501687 1.42097698 0.620082593 1.74879124 1.201562470
## 114 1.388253402 1.23011687 1.575707258 0.92592015 1.671940012
## 115 1.453325144 1.42097698 0.774353839 1.36328177 1.455249234
## 116 1.364279602 1.25302009 1.280020703 1.32270183 1.333690993
## 117 1.754710054 1.76452517 0.435814160 1.92238322 1.566237194
## 118 1.826631454 1.77215957 0.804351026 1.52109266 1.843707092
## 119 1.107417462 1.16904164 0.268686976 0.94395568 0.789321478
## 120 0.432726241 0.60409573 -0.519810505 0.27438661 0.324229076
## 121 1.206737489 1.05452558 1.575707258 0.76585482 1.338976134
## 122 0.371079328 0.23000993 1.519998197 -0.62513548 0.839530317
## 123 1.316332002 1.21484807 1.241452891 1.06118663 1.597948039
## 124 1.282083717 1.39043936 -0.048426141 1.45345942 0.871241162
## 125 1.052620206 0.96291273 1.215741017 0.76360038 1.117000215
## 126 1.799232825 1.86377242 0.110130418 1.96747205 1.344261275
## 127 0.905352579 0.81785905 1.207170392 0.33976541 1.124927926
## 128 1.162214719 0.99345035 1.708551942 0.76360038 1.296695006
## 129 1.381403745 1.32172972 0.997190084 1.13558320 1.481674939
## 130 0.162164787 0.23764433 -0.014143642 0.54942845 0.009763191
## 131 0.429301413 0.56592371 -0.279833011 0.46150524 0.345369640
## 132 0.223811701 0.23000993 0.487237909 0.30594879 0.379723056
## 133 0.840280837 0.89420309 0.324396038 1.13783764 0.818389753
## 134 0.227236530 0.42850444 -0.781214561 0.63058834 -0.088011916
## 135 0.237511015 0.39796682 -0.554093004 0.43219750 0.057329460
## 136 0.453275213 0.45140765 0.602941344 0.52237516 0.548847566
## 137 -0.628970603 -0.51052727 -0.982624244 -0.37940137 -0.714301115
## 138 -0.543349890 -0.49525846 -0.412677695 -0.22384492 -0.505538049
## 139 -0.536500233 -0.48762406 -0.382680508 -0.56652000 -0.502895478
## 140 -0.920081029 -0.96859152 -0.245550511 -0.93850282 -0.785650518
## 141 -1.057074170 -0.90751629 -1.865398598 -0.73560310 -1.287738906
## 142 -1.265988710 -1.11364520 -2.323927025 -0.81450855 -1.525570247
## 143 -1.190642482 -1.11364520 -1.603994542 -1.04671601 -1.441007992
## 144 -0.827610658 -0.86170986 -0.219838637 -0.84156185 -0.785650518
## 145 -0.755689259 -0.67084976 -0.935485808 -0.57328333 -0.933634464
## 146 -1.409831508 -1.26633328 -2.581045769 -0.72883978 -1.628630495
## 147 -1.053649341 -1.03730116 -0.914059246 -0.85057961 -1.121256966
## 148 -0.992002428 -0.81590344 -1.972531408 -0.53044894 -1.290381476
## 149 -0.906381714 -0.75482821 -1.612565167 -0.52368562 -1.139754960
## 150 -1.276263196 -1.18235484 -1.899681097 -0.94752059 -1.509714824
## 151 -1.214616282 -1.17472043 -1.402584859 -1.04897045 -1.345875456
## 152 -1.272838367 -1.17472043 -1.955390159 -0.87988735 -1.557281093
## 153 -1.221465939 -1.31213971 -0.485528006 -1.32401229 -1.097473832
## 154 -0.950904485 -0.65558095 -2.692463891 -0.55524780 -1.372301160
## 155 -1.081047969 -0.81590344 -2.688178579 -0.44252573 -1.549353381
## 156 -1.170093511 -1.04493556 -1.912537035 -0.75814752 -1.504429684
## 157 -0.810486515 -0.70138738 -1.218316426 -0.42674464 -1.018196718
## 158 -0.988577599 -0.96095712 -0.884062059 -0.65444322 -1.200534080
## 159 -0.978303114 -0.89988188 -1.256884238 -0.84156185 -1.102758973
## 160 -0.807061687 -0.77773142 -0.648369877 -0.69276872 -0.783007947
## 161 -1.289962510 -1.37321494 -0.648369877 -1.41869883 -1.242815208
## 162 -0.961178971 -1.09837639 0.358678537 -1.20678134 -0.854357350
## 163 -0.844734801 -0.76246261 -1.059759867 -0.72433090 -0.970630450
## 164 -0.944054828 -0.86934427 -1.141180803 -0.50339565 -1.129184678
## 165 -1.218041111 -1.12127961 -1.792548287 -1.04671601 -1.575779086
## 166 -1.149544540 -1.13654841 -1.089757054 -1.06700598 -1.108044114
## 167 -1.245439739 -1.23579567 -1.244028300 -1.24510684 -1.324734892
## 168 -1.200916968 -1.15181722 -1.432582046 -1.13689366 -1.324734892
## 169 -1.396132194 -1.24343007 -2.615328268 -0.81676299 -1.647128488
## 170 -1.406406680 -1.54117183 -0.511239880 -1.48633207 -1.171465805
## 171 -1.248864568 -1.20525805 -1.518288294 -1.02417159 -1.464791127
## 172 -1.426955651 -1.41902137 -1.629706416 -1.11660369 -1.644485918
## 173 -1.173518340 -1.17472043 -1.012621431 -1.03769824 -1.337947744
## 174 -0.923505857 -0.85407546 -1.098327679 -0.63866213 -0.981200732
## 175 -1.197492140 -1.25106448 -0.639799252 -1.24059796 -1.293024047
## 176 -0.837885144 -0.89988188 -0.014143642 -0.91144953 -0.653521995
## 177 -0.930355514 -0.93805391 -0.558378317 -0.90243176 -0.938919605
## 178 -1.115296255 -1.15945163 -0.575519566 -1.20001802 -1.094831262
## 179 -0.690617517 -0.62504334 -0.721220188 -0.32754922 -0.629738861
## 180 -1.146119712 -0.97622593 -2.191082341 -0.62513548 -1.536140529
## 181 -1.060498998 -0.86934427 -2.191082341 -0.51241341 -1.448935704
## 182 -1.368733566 -1.36558054 -1.445437983 -1.24510684 -1.557281093
## 183 -1.259139053 -1.35031173 -0.494098631 -1.24285240 -1.171465805
## 184 -1.478328079 -1.66332230 -0.262691761 -1.67119625 -1.261313201
## 185 -1.361883909 -1.36558054 -1.368302360 -1.33979338 -1.446293133
## 186 -1.245439739 -1.31977411 -0.626943315 -1.23834352 -1.216389503
## 187 -1.039950027 -1.19762365 0.367249162 -1.14591143 -0.822646504
## 188 -1.399557023 -1.34267733 -1.942534221 -1.03769824 -1.676196764
## 189 -0.957754142 -1.00676354 -0.301259573 -0.91144953 -0.764509954
## 190 -0.721440974 -0.85407546 0.645794468 -1.08278707 -0.365481814
## 191 -0.724865802 -0.80826903 0.328681350 -0.93850282 -0.555746888
## 192 -0.526225747 -0.61740893 0.598656031 -0.72207646 -0.360196673
## 193 -0.783087887 -0.70138738 -0.978338932 -0.51917674 -0.933634464
## 194 -0.735140288 -0.92278510 1.091466957 -1.27892346 -0.288847271
## 195 -0.865283772 -0.87697867 -0.429818945 -0.94977503 -0.727513968
## 196 -1.276263196 -1.42665577 -0.125561764 -1.42320771 -1.200534080
## 197 -0.755689259 -0.89988188 0.705788841 -1.03093492 -0.457971780
## 198 -0.868708601 -0.85407546 -0.609802065 -0.98359165 -0.804148511
## 199 -0.930355514 -1.06020437 0.315825413 -1.13463922 -0.748654531
## 200 -1.259139053 -1.30450530 -0.849779560 -1.12787589 -1.240172637
## 201 -0.584447832 -0.70902178 0.744356653 -0.91144953 -0.085369345
## 202 -1.050224513 -1.05256997 -0.806926436 -1.04897045 -1.131827248
## 203 -0.892682400 -0.95332271 -0.108420515 -0.89566844 -0.767152525
## asymetry_coefficient length_of_kernel_groove cluster
## 1 -0.990010499 -0.40539297 1
## 2 -1.789636480 -0.94117151 1
## 3 -0.672287126 -1.20703132 1
## 4 -0.964752156 -1.24762060 1
## 5 -1.565634854 -0.49671885 1
## 6 -0.829819426 -0.94117151 1
## 7 -0.082704212 -0.40742243 1
## 8 -1.110319978 0.92796500 2
## 9 -1.157513199 0.22982932 2
## 10 0.553407229 -0.21462333 3
## 11 -1.325015898 -0.84984562 1
## 12 0.183173088 -1.38359470 3
## 13 -0.381816175 -1.11164650 3
## 14 -0.517413598 -1.20703132 3
## 15 0.315447045 -1.29632774 3
## 16 1.012710265 -1.29632774 3
## 17 -1.403449701 -0.75851973 1
## 18 -1.291781235 -1.56421701 1
## 19 0.260277505 -1.02640900 3
## 20 -0.424356543 -0.49468939 1
## 21 -0.679598751 -0.40742243 1
## 22 -1.957737403 -0.66719385 1
## 23 -1.525753259 -0.93102419 1
## 24 -1.275828597 -0.84984562 1
## 25 -1.866076204 -0.22882958 1
## 26 -0.224283875 -1.20703132 3
## 27 -0.801902309 -1.11773489 3
## 28 -0.635728997 -0.77475545 1
## 29 -0.119262341 -0.65501706 3
## 30 -1.897915011 -0.73822509 1
## 31 -0.198360838 -0.38915725 2
## 32 0.139303334 -0.24506530 3
## 33 -1.054485745 -0.82752152 1
## 34 -1.051162278 -0.12126798 1
## 35 -0.589865162 0.21765253 2
## 36 -0.501460960 0.13038558 1
## 37 -0.397768813 -0.22477066 1
## 38 1.977180172 -0.84984562 3
## 39 -0.746732770 -0.49063046 1
## 40 -0.971399088 -0.49468939 1
## 41 -0.830484119 -1.29226881 1
## 42 0.665075695 0.21968200 2
## 43 -0.387133721 -0.48657153 1
## 44 -1.429372738 -0.93102419 1
## 45 -1.557658535 -0.58398581 1
## 46 -0.500131574 -0.49671885 1
## 47 -0.668963659 -0.62660456 1
## 48 -1.051162278 -0.13953316 1
## 49 0.175861462 -0.55963224 1
## 50 1.251335142 -0.57586796 3
## 51 0.269583211 -0.04820727 3
## 52 -0.254195071 -0.39727511 1
## 53 -1.481883505 0.13647397 1
## 54 -1.181442156 0.03905969 1
## 55 -0.603823721 -0.76257866 1
## 56 -1.707214517 -0.67328224 1
## 57 -1.137572401 -0.40133404 1
## 58 -1.467924947 -1.82804735 1
## 59 -0.958105223 -1.45462595 1
## 60 -0.325981942 -1.65351343 1
## 61 0.296835634 -0.67328224 3
## 62 -1.684614946 -1.29429828 1
## 63 -0.902935684 -1.64945451 1
## 64 -1.593551971 -0.54745546 1
## 65 -0.992669272 -0.58398581 1
## 66 -1.493183290 -0.24303583 1
## 67 -0.117932954 -0.71590099 3
## 68 0.242995481 1.29935694 2
## 69 0.641146738 0.92796500 2
## 70 0.550748456 0.75343108 2
## 71 -0.514754825 1.33791676 2
## 72 0.195137567 0.85490429 2
## 73 0.803996584 0.76154893 2
## 74 0.075492782 1.01929088 2
## 75 0.492255450 2.09287743 2
## 76 0.899712412 1.91225512 2
## 77 -0.566600899 0.66210519 2
## 78 1.210788854 0.93405339 2
## 79 1.072532658 0.93202392 2
## 80 0.972163977 1.55709889 2
## 81 -1.487865744 1.73163281 2
## 82 -0.497472801 1.55303996 2
## 83 -0.842448598 1.57739353 2
## 84 -1.370215039 1.39880068 2
## 85 -0.012911421 2.18826225 2
## 86 1.375632780 1.64639531 2
## 87 0.867807136 1.82904709 2
## 88 -1.166818904 2.08881850 2
## 89 -0.392451267 1.28515069 2
## 90 -0.314682157 1.28515069 2
## 91 1.522529988 0.92796500 2
## 92 0.812637597 2.08678904 2
## 93 -0.009587955 1.11061677 2
## 94 -0.155155776 1.66060156 2
## 95 -1.041191880 2.09693636 2
## 96 -0.569924365 1.73163281 2
## 97 -1.011945377 1.37444711 2
## 98 0.336717229 0.40233377 2
## 99 -1.091708567 0.84678643 2
## 100 0.397204315 1.19585427 2
## 101 -0.234918967 1.64233639 2
## 102 -0.227607341 1.47794979 2
## 103 -0.769332340 0.93202392 2
## 104 -0.576571297 1.58348192 2
## 105 0.024311401 1.03349713 2
## 106 -0.304711758 2.29379439 2
## 107 -1.311057339 0.96246589 2
## 108 -0.980704794 0.75951947 2
## 109 -0.021552433 1.10655784 2
## 110 -1.064456143 1.02334981 2
## 111 1.975186092 1.28515069 2
## 112 0.642476124 1.81889977 2
## 113 -0.972063782 1.50839175 2
## 114 0.414486339 0.67022305 2
## 115 -0.416380224 1.55303996 2
## 116 -0.047475470 1.10858731 2
## 117 -0.430338782 1.82701763 2
## 118 1.456060663 1.55912835 2
## 119 -0.060769335 1.19991320 2
## 120 0.382581063 0.57483823 2
## 121 -0.482849549 0.98478999 2
## 122 -0.248877525 -0.55963224 1
## 123 0.317441125 1.16135338 2
## 124 0.452373855 1.38459444 2
## 125 -0.966081542 1.01320249 2
## 126 -1.196730100 1.55303996 2
## 127 1.100449774 0.48960073 2
## 128 -0.580559457 1.10046945 2
## 129 -0.533366236 1.07408642 2
## 130 0.499567075 0.76154893 2
## 131 0.369287198 0.76154893 2
## 132 0.838560633 0.86708107 2
## 133 -0.122585807 1.11873463 2
## 134 -0.711504027 0.93202392 2
## 135 -0.655005101 0.67428197 2
## 136 0.038934653 1.01929088 2
## 137 1.059238793 -0.05023674 3
## 138 2.209822810 0.04108915 3
## 139 1.518541829 -0.22882958 3
## 140 1.168913179 -0.40336350 3
## 141 0.505549315 -0.49063046 3
## 142 1.634198454 -0.29377244 3
## 143 -0.990010499 -0.58398581 3
## 144 0.472314652 -0.84781616 3
## 145 -0.299394212 -0.21056441 3
## 146 1.164260326 -0.45815904 3
## 147 0.986787228 -0.22882958 3
## 148 2.181241000 -0.30391976 3
## 149 0.694986891 -0.12126798 3
## 150 -0.251536298 -0.84984562 3
## 151 0.224384070 -0.31812601 3
## 152 1.397567657 -0.40742243 3
## 153 -0.241565899 -0.84578669 3
## 154 0.740850725 -0.40539297 3
## 155 0.443732842 -0.22274119 3
## 156 1.115073026 -0.22274119 3
## 157 -0.417709611 0.14459182 3
## 158 0.372610665 -0.22680012 3
## 159 0.849195725 -0.75851973 3
## 160 0.470985266 -0.49468939 3
## 161 1.779766276 -0.75243134 3
## 162 -1.003304364 -0.73822509 1
## 163 0.806655357 -0.30391976 3
## 164 -0.048140163 -0.16591619 3
## 165 0.416480419 -0.58398581 3
## 166 1.997120969 -0.94117151 3
## 167 0.397869008 -0.85187509 3
## 168 1.248011676 -0.67125277 3
## 169 0.978146216 -0.47642421 3
## 170 0.706286676 -0.72401884 3
## 171 1.079844283 -0.66516438 3
## 172 0.659093456 -0.92696526 3
## 173 1.439443332 -0.84781616 3
## 174 -1.362238720 -0.49063046 3
## 175 0.828590235 -1.20703132 3
## 176 0.848531032 -0.55354385 3
## 177 0.762120910 -0.53121975 3
## 178 0.996092934 -0.57789742 3
## 179 1.644833546 -0.21056441 3
## 180 0.233689775 -0.48251261 3
## 181 0.789373333 -0.13750370 3
## 182 0.311458886 -0.94117151 3
## 183 2.534857809 -0.93914205 3
## 184 0.840554713 -1.26994471 3
## 185 1.121719958 -0.76054920 3
## 186 0.182508395 -0.84984562 3
## 187 -0.075392586 -0.58398581 3
## 188 0.759462137 -0.67125277 3
## 189 0.280218303 -0.82752152 3
## 190 0.772756002 -1.02640900 3
## 191 1.178218884 -0.93711258 3
## 192 0.637823272 -0.66719385 3
## 193 -0.268818322 -0.38306886 3
## 194 -0.586541696 -1.19688400 1
## 195 1.170907259 -0.76054920 3
## 196 0.226378149 -1.20094292 3
## 197 3.154351919 -0.85187509 3
## 198 0.138638640 -0.84984562 3
## 199 -0.052793016 -1.11570543 3
## 200 0.408504100 -0.84578669 3
## 201 3.060630171 -0.73822509 3
## 202 -0.074727893 -0.76257866 3
## 203 1.280581645 -0.72401884 3count(seeds_df_cl,cluster)## cluster n
## 1 1 49
## 2 2 73
## 3 3 81library(ggplot2)
ggplot(seeds_df_cl, aes(x=area, y = perimeter, color = factor(cluster))) +geom_point()
library(clValid)## Loading required package: clusterdunn(dist_mat,cut_avg)## [1] 0.105588#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$betweenss## [1] 602.5192table(ir3$cluster, iris$Species)##
## setosa versicolor virginica
## 1 50 0 0
## 2 0 48 14
## 3 0 2 36cm<- table(ir3$cluster, iris$Species)
1-sum(diag(cm))/sum(cm)## [1] 0.1066667