Classwork Clustering
Bezawit Tilahun
2024-02-27
library(readr)
seeds_dataset <- read_table("Documents/seeds_dataset.txt",
col_names = FALSE)##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## X1 = col_double(),
## X2 = col_double(),
## X3 = col_double(),
## X4 = col_double(),
## X5 = col_double(),
## X6 = col_double(),
## X7 = col_double(),
## X8 = col_double()
## )View(seeds_dataset)
sd <- seeds_dataset
str(seeds_dataset)## 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: num [1:210] 1 1 1 1 1 1 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_double()
## .. )#Rename the columns
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. :4.899
## 1st Qu.:12.27 1st Qu.:13.45 1st Qu.:0.8569 1st Qu.:5.262
## Median :14.36 Median :14.32 Median :0.8734 Median :5.524
## Mean :14.85 Mean :14.56 Mean :0.8710 Mean :5.629
## 3rd Qu.:17.30 3rd Qu.:15.71 3rd Qu.:0.8878 3rd Qu.:5.980
## Max. :21.18 Max. :17.25 Max. :0.9183 Max. :6.675
## width.of.kernel asymetry coefficient length.of.kernel.groove type of seed
## Min. :2.630 Min. :0.7651 Min. :4.519 Min. :1
## 1st Qu.:2.944 1st Qu.:2.5615 1st Qu.:5.045 1st Qu.:1
## Median :3.237 Median :3.5990 Median :5.223 Median :2
## Mean :3.259 Mean :3.7002 Mean :5.408 Mean :2
## 3rd Qu.:3.562 3rd Qu.:4.7687 3rd Qu.:5.877 3rd Qu.:3
## Max. :4.033 Max. :8.4560 Max. :6.550 Max. :3#where do we have N/As in the dataset?
any(is.na(sd))## [1] FALSE#remove rows with N/As
sd<-na.omit(sd)
str(sd)## tibble [210 × 8] (S3: tbl_df/tbl/data.frame)
## $ area : num [1:210] 15.3 14.9 14.3 13.8 16.1 ...
## $ perimeter : num [1:210] 14.8 14.6 14.1 13.9 15 ...
## $ compactness : num [1:210] 0.871 0.881 0.905 0.895 0.903 ...
## $ length_of_kernel : num [1:210] 5.76 5.55 5.29 5.32 5.66 ...
## $ width.of.kernel : num [1:210] 3.31 3.33 3.34 3.38 3.56 ...
## $ asymetry coefficient : num [1:210] 2.22 1.02 2.7 2.26 1.35 ...
## $ length.of.kernel.groove: num [1:210] 5.22 4.96 4.83 4.8 5.17 ...
## $ type of seed : num [1:210] 1 1 1 1 1 1 1 1 1 1 ...#remove last column
sd <- sd[,-8]
View(sd)
#Scaling the features
df_sc <- as.data.frame(scale(sd))
str(df_sc)## 'data.frame': 210 obs. of 7 variables:
## $ area : num 0.1418 0.0112 -0.1916 -0.3463 0.4442 ...
## $ perimeter : num 0.2149 0.0082 -0.3593 -0.4742 0.3298 ...
## $ compactness : num 6.05e-05 4.27e-01 1.44 1.04 1.37 ...
## $ length_of_kernel : num 0.3035 -0.1682 -0.7618 -0.6873 0.0665 ...
## $ width.of.kernel : num 0.141 0.197 0.208 0.319 0.803 ...
## $ asymetry coefficient : num -0.984 -1.784 -0.666 -0.959 -1.56 ...
## $ length.of.kernel.groove: num -0.383 -0.92 -1.186 -1.227 -0.474 ...#Build distance matrix
dist_mat <- dist(df_sc, method = 'euclidean')
#plotting the cluster
hclust_avg <- hclust(dist_mat, method = 'average')
plot(hclust_avg)
#Visualizing all branches
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)
cut_avg <- cutree(hclust_avg, k = 3)
#Evaluating the clustering
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 0.141759037 0.2149488188 6.045733e-05 0.303493006 0.141364035
## 2 0.011161356 0.0082041534 4.274938e-01 -0.168222697 0.196961591
## 3 -0.191608729 -0.3593419185 1.438945e+00 -0.761817099 0.207551602
## 4 -0.346263878 -0.4742000660 1.036904e+00 -0.687335672 0.318746714
## 5 0.444195774 0.3298069663 1.371233e+00 0.066506648 0.803239702
## 6 -0.160677699 -0.2674554005 1.019976e+00 -0.547400870 0.141364035
## 7 -0.054137485 -0.0530535253 3.767096e-01 -0.147909581 0.001046394
## 8 -0.253470789 -0.3516847087 8.506951e-01 -0.470662431 0.114889009
## 9 0.612598048 0.6896958284 1.566449e-01 0.958026757 0.546431943
## 10 0.547299207 0.5288944219 7.195027e-01 0.576591571 0.652332050
## 11 0.141759037 0.2226060287 -5.918773e-02 0.192899372 -0.043961151
## 12 -0.280965037 -0.3057414497 3.640136e-01 -0.430036198 -0.152508761
## 13 -0.329079973 -0.4129423873 7.195027e-01 -0.427779185 -0.157803766
## 14 -0.366884565 -0.3823135480 2.074291e-01 -0.337498667 -0.271646381
## 15 -0.380631689 -0.3899707578 1.439489e-01 -0.330727629 -0.382841493
## 16 -0.088505296 -0.2138549317 1.197720e+00 -0.626396323 0.196961591
## 17 -0.294712162 -0.5584293741 2.001803e+00 -1.150023324 0.329336725
## 18 0.289540625 0.1460339303 1.472801e+00 -0.229162047 0.676159574
## 19 -0.050700704 -0.2674554005 1.874842e+00 -0.955920211 0.549079446
## 20 -0.731183361 -0.7575168297 -1.015079e-01 -0.908522940 -0.554929166
## 21 -0.236286883 -0.1219684137 -5.331732e-01 0.066506648 -0.343128953
## 22 -0.253470789 -0.2291693514 5.084461e-02 -0.244961137 -0.239876349
## 23 0.354839465 0.2608920778 1.176560e+00 -0.023773870 0.657627055
## 24 -0.951137351 -1.0178619640 -1.946121e-01 -1.195163583 -0.854096967
## 25 0.055839510 0.1536911402 -2.242362e-01 0.362175342 -0.036018643
## 26 0.461379679 0.4599795334 5.883103e-01 0.461483911 0.429941826
## 27 -0.628079928 -0.6120298429 -2.919484e-01 -0.527087754 -0.615821727
## 28 -0.724309799 -0.6809447314 -6.178135e-01 -0.527087754 -0.801146914
## 29 -0.253470789 -0.2904270300 4.655819e-01 -0.197563866 -0.099558707
## 30 -0.480298341 -0.4129423873 -4.485329e-01 -0.253989189 -0.512569123
## 31 -0.579964993 -0.5660865839 -2.030762e-01 -0.393923991 -0.750844363
## 32 0.220805003 0.2915209171 5.930864e-02 0.289950928 0.297566693
## 33 -0.260344351 -0.1143112039 -7.659339e-01 0.199670411 -0.192221301
## 34 -0.311896067 -0.2980842399 7.623669e-02 -0.098255296 -0.287531397
## 35 0.069586634 0.0924334615 2.920694e-01 0.188385346 0.183724078
## 36 0.437322212 0.3374641761 1.227344e+00 0.181614307 0.599381997
## 37 0.464816460 0.5442088416 1.016288e-01 0.445684821 0.543784441
## 38 0.767253197 0.6284381497 1.561673e+00 0.459226898 1.123587524
## 39 -0.016332893 -0.0300818958 4.782779e-01 0.061992622 0.077823971
## 40 -0.195045510 -0.2980842399 9.903515e-01 -0.522573728 0.104298998
## 41 -0.449367311 -0.5431149544 6.814146e-01 -0.633167362 -0.271646381
## 42 -0.463114436 -0.5431149544 6.010063e-01 -0.626396323 -0.266351375
## 43 -0.579964993 -0.7728312494 1.265432e+00 -1.107140078 -0.152508761
## 44 0.224241784 0.2302632385 4.655819e-01 0.560792480 0.363754259
## 45 0.090207321 -0.0147674761 1.168096e+00 -0.111797374 0.538489435
## 46 -0.360011003 -0.3976279677 3.555496e-01 -0.569971000 -0.274293883
## 47 0.176126848 0.1536911402 6.390944e-01 0.163558204 0.355811751
## 48 0.048965948 0.0005469436 7.321987e-01 -0.132110490 0.313451709
## 49 -0.019769674 -0.0300818958 4.613499e-01 -0.188535814 0.085766479
## 50 0.004287794 0.0847762517 -1.438280e-01 0.111646906 -0.001601109
## 51 -0.143493794 -0.1219684137 1.735730e-01 -0.098255296 0.035463929
## 52 0.320471654 0.2685492876 9.014792e-01 0.102618855 0.464359361
## 53 -0.122873107 0.0388329927 -7.278458e-01 0.195156385 -0.385488995
## 54 -0.177861605 -0.2138549317 5.121340e-01 -0.281073344 -0.157803766
## 55 -0.112562764 0.0311757829 -6.474375e-01 0.253838721 -0.385488995
## 56 0.062713072 0.1613483500 -2.200042e-01 0.165815217 -0.123386231
## 57 -0.133183450 -0.1602544629 4.571179e-01 -0.542886844 0.313451709
## 58 0.024908480 -0.0989967842 1.252736e+00 -0.551914896 0.406114302
## 59 0.183000411 0.1613483500 6.221664e-01 0.075534700 0.424646821
## 60 -0.940827008 -0.8340889280 -1.345720e+00 -1.059742806 -0.599936711
## 61 -1.177964904 -1.3011787277 -1.142039e-01 -1.400551760 -1.081782197
## 62 -1.243263744 -1.4772945538 5.502222e-01 -1.639795131 -1.005004619
## 63 -0.854907481 -1.0484908033 9.014792e-01 -1.247074880 -0.573461685
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## 67 -0.174424823 -0.1449400432 6.777267e-02 0.003310286 -0.181631290
## 68 -0.287838600 -0.2061977219 -3.596607e-01 -0.044086986 -0.266351375
## 69 -0.164114480 -0.1296256236 6.777267e-02 -0.134367503 -0.279588889
## 70 -0.727746580 -0.6196870528 -1.066407e+00 -0.488718534 -0.997062111
## 71 0.956276157 1.0878707396 -1.565240e-01 1.269494542 0.800592199
## 72 0.684770451 0.8504972348 -3.681247e-01 0.833891046 0.596734494
## 73 0.829115257 0.8964404938 2.243572e-01 0.788750787 0.887959787
## 74 1.464919760 1.3022726148 1.570137e+00 1.185985063 1.777520683
## 75 0.677896888 0.7279818775 3.216935e-01 0.876774291 0.602029499
## 76 0.660712983 0.8122111857 -3.046445e-01 0.673643127 0.474949371
## 77 0.849735943 1.0342702708 -4.696930e-01 0.982853899 0.382286778
## 78 2.014804735 2.0450219685 2.243572e-01 2.145215560 1.470410374
## 79 1.406494481 1.4783884410 1.693410e-01 1.842775827 1.007097407
## 80 0.781000321 0.7586107168 7.702868e-01 0.499853131 0.813829712
## 81 0.578230237 0.5978093104 4.782779e-01 0.556278455 0.551726949
## 82 1.330885297 1.2486721460 1.130008e+00 0.851947149 1.584252988
## 83 1.839528899 1.7846768342 7.787509e-01 1.481653758 1.602785507
## 84 1.623011690 1.6698186867 2.920694e-01 1.705098038 1.359215262
## 85 1.602391003 1.6468470572 2.963014e-01 1.664471805 1.435992839
## 86 1.176230147 1.1721000477 6.771825e-01 1.228868309 1.038867439
## 87 1.385873794 1.3022726148 1.096152e+00 1.027994158 1.338035240
## 88 1.420241605 1.6085610081 -5.077811e-01 2.077505172 1.089169990
## 89 2.176333447 2.0297075488 1.180792e+00 2.131673483 2.050213458
## 90 2.073230014 1.9071921915 1.358537e+00 1.854060892 2.047565955
## 91 1.805161088 1.8612489325 1.524129e-01 2.149729586 1.393632796
## 92 1.344632421 1.2563293559 1.159632e+00 1.226611296 1.422755326
## 93 1.361816327 1.3252442443 8.295350e-01 1.452312590 1.150062551
## 94 1.286207143 1.1414712084 1.506657e+00 0.921914550 1.592195496
## 95 1.207161177 1.5013600704 -1.091799e+00 2.341575686 0.599381997
## 96 0.695080794 0.8351828151 -2.623244e-01 1.152129869 0.541136938
## 97 1.533655382 1.5549605393 4.444218e-01 1.608046482 1.459820363
## 98 1.420241605 1.5396461196 -9.727584e-02 1.851803879 0.776764675
## 99 1.141862337 1.3022726148 -3.088765e-01 1.450055577 0.670864568
## 100 1.330885297 1.3635302935 4.232618e-01 1.332690904 1.126235027
## 101 0.536988864 0.5288944219 6.602545e-01 0.201927424 0.705282103
## 102 1.080000277 0.9959842216 1.193488e+00 0.590133649 1.152710054
## 103 1.585207098 1.4860456508 1.163864e+00 1.093447533 1.676915582
## 104 1.488977227 1.5855893786 2.968455e-02 1.671242844 1.118292519
## 105 1.409931262 1.4247879721 5.036700e-01 1.398144279 1.314207716
## 106 1.368689889 1.3252442443 8.760872e-01 0.921914550 1.396280299
## 107 1.375563451 1.2333577264 1.464337e+00 1.181471037 1.449230352
## 108 0.956276157 0.9959842216 3.809416e-01 0.912886498 0.832362231
## 109 1.750172591 1.8076484637 1.778050e-01 2.361888802 1.335387738
## 110 1.272460018 1.2716437755 6.560225e-01 1.183728050 1.099760000
## 111 1.238092207 1.1950716772 8.930152e-01 1.079905455 1.351272754
## 112 1.557712849 1.6545042671 2.545254e-02 1.522279991 1.409517812
## 113 1.471793322 1.3405586640 1.375465e+00 1.251438438 1.703390608
## 114 1.475230103 1.5702749589 5.084461e-02 1.422971421 1.266552668
## 115 2.104161044 2.0603363881 6.306304e-01 2.109103353 1.939018346
## 116 1.447735854 1.4477596016 6.094703e-01 1.777322452 1.218897620
## 117 1.413368043 1.2563293559 1.553209e+00 0.953512731 1.690153095
## 118 1.478666884 1.4477596016 7.618228e-01 1.391373240 1.473057876
## 119 1.389310575 1.2793009853 1.261200e+00 1.350747007 1.351272754
## 120 1.781103620 1.7923340440 4.274938e-01 1.951112448 1.584252988
## 121 1.853276023 1.7999912539 7.914469e-01 1.549364146 1.862240768
## 122 1.131551993 1.1950716772 2.624453e-01 0.971568835 0.805887204
## 123 0.454506117 0.6284381497 -5.162451e-01 0.301235993 0.339926735
## 124 1.231218645 1.0802135297 1.553209e+00 0.793264813 1.356567759
## 125 0.392644057 0.2532348680 1.498193e+00 -0.599312168 0.856189755
## 126 1.341195640 1.2410149362 1.223112e+00 1.088933507 1.616023020
## 127 1.306827829 1.4171307623 -5.072370e-02 1.481653758 0.887959787
## 128 1.076563496 0.9883270118 1.197720e+00 0.791007800 1.134177535
## 129 1.825781775 1.8918777718 1.058608e-01 1.996252707 1.361862764
## 130 0.928781909 0.8428400250 1.189256e+00 0.366689368 1.142120043
## 131 1.186540491 1.0189558511 1.684402e+00 0.791007800 1.314207716
## 132 1.406494481 1.3482158738 9.818875e-01 1.163414934 1.499532903
## 133 0.183000411 0.2608920778 -1.686759e-02 0.576591571 0.024873918
## 134 0.451069336 0.5901521006 -2.792524e-01 0.488568067 0.361106757
## 135 0.244862470 0.2532348680 4.782779e-01 0.332834174 0.395524291
## 136 0.183000411 0.0771190419 1.185024e+00 -0.342012693 0.546431943
## 137 0.863483068 0.9194121233 3.174614e-01 1.165671947 0.835009734
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## 146 -1.250137307 -1.0944340623 -2.297923e+00 -0.788901254 -1.513325131
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## 162 -0.971758038 -0.9412898657 -8.759662e-01 -0.628653336 -1.187682303
## 163 -0.961447695 -0.8800321870 -1.244151e+00 -0.815985410 -1.089724705
## 164 -0.789608640 -0.7575168297 -6.432055e-01 -0.667022556 -0.769376882
## 165 -1.274194774 -1.3547791965 -6.432055e-01 -1.393780721 -1.230042346
## 166 -0.944263789 -1.0791196426 3.513175e-01 -1.181621505 -0.840859454
## 167 -0.827413232 -0.7422024101 -1.049479e+00 -0.698620737 -0.957349571
## 168 -0.927079884 -0.8494033477 -1.129887e+00 -0.477433469 -1.116199731
## 169 -1.202022371 -1.1020912721 -1.773153e+00 -1.021373587 -1.563627682
## 170 -1.239826963 -1.1939777901 -1.485376e+00 -1.215476699 -1.439195057
## 171 -1.315436148 -1.1939777901 -2.204818e+00 -0.685078659 -1.476260094
## 172 -1.133286749 -1.1174056918 -1.079103e+00 -1.041686703 -1.095019710
## 173 -1.229516620 -1.2169494196 -1.231455e+00 -1.219990725 -1.312114929
## 174 -1.184838466 -1.1327201114 -1.417664e+00 -1.111654104 -1.312114929
## 175 -1.380734988 -1.2246066294 -2.585700e+00 -0.791158267 -1.635110254
## 176 -1.391045332 -1.5232378128 -5.077811e-01 -1.461491109 -1.158559774
## 177 -1.232953401 -1.1863205803 -1.502304e+00 -0.998803457 -1.452432570
## 178 -1.411666018 -1.4007224555 -1.612337e+00 -1.091340988 -1.632462751
## 179 -1.157344217 -1.1556917409 -1.002927e+00 -1.012345535 -1.325352442
## 180 -0.906459197 -0.8340889280 -1.087567e+00 -0.612854245 -0.967939582
## 181 -1.181401685 -1.2322638392 -6.347415e-01 -1.215476699 -1.280344897
## 182 -0.820539670 -0.8800321870 -1.686759e-02 -0.885952811 -0.639649251
## 183 -0.913332759 -0.9183182362 -5.543333e-01 -0.876924759 -0.925579539
## 184 -1.098918938 -1.1403773213 -5.712613e-01 -1.174850466 -1.081782197
## 185 -0.672758082 -0.6043726331 -7.151498e-01 -0.301386461 -0.615821727
## 186 -1.129849968 -0.9566042853 -2.166730e+00 -0.599312168 -1.523915142
## 187 -1.043930441 -0.8494033477 -2.166730e+00 -0.486461521 -1.436547554
## 188 -1.353240740 -1.3471219867 -1.430360e+00 -1.219990725 -1.545095163
## 189 -1.243263744 -1.3318075670 -4.908531e-01 -1.217733712 -1.158559774
## 190 -1.463217735 -1.6457531701 -2.623244e-01 -1.646566169 -1.248574865
## 191 -1.346367177 -1.3471219867 -1.354184e+00 -1.314785268 -1.433900051
## 192 -1.229516620 -1.3011787277 -6.220455e-01 -1.213219686 -1.203567319
## 193 -1.023309754 -1.1786633704 3.597816e-01 -1.120682156 -0.809089422
## 194 -1.384171769 -1.3241503572 -1.921273e+00 -1.012345535 -1.664232783
## 195 -0.940827008 -0.9872331246 -3.004125e-01 -0.885952811 -0.750844363
## 196 -0.703689112 -0.8340889280 6.348624e-01 -1.057485794 -0.351071461
## 197 -0.707125893 -0.7881456690 3.216935e-01 -0.913036966 -0.541691653
## 198 -0.507792590 -0.5967154233 5.883103e-01 -0.696363724 -0.345776455
## 199 -0.765551172 -0.6809447314 -9.690705e-01 -0.493232560 -0.920284534
## 200 -0.717436237 -0.9030038165 1.074992e+00 -1.253845919 -0.274293883
## 201 -0.848033918 -0.8570605575 -4.273729e-01 -0.924322030 -0.713779326
## 202 -0.748367267 -0.9489470755 1.130008e+00 -1.454720070 -0.327243937
## 203 -1.260447650 -1.4083796654 -1.268999e-01 -1.398294747 -1.187682303
## 204 -0.738056923 -0.8800321870 6.941106e-01 -1.005574496 -0.443734054
## 205 -0.851470699 -0.8340889280 -6.051174e-01 -0.958177224 -0.790556903
## 206 -0.913332759 -1.0408335935 3.089974e-01 -1.109397091 -0.734959347
## 207 -1.243263744 -1.2858643081 -8.421101e-01 -1.102626052 -1.227394843
## 208 -0.566217868 -0.6886019412 7.321987e-01 -0.885952811 -0.070436178
## 209 -1.033620098 -1.0331763836 -7.997900e-01 -1.023630600 -1.118847234
## 210 -0.875528167 -0.9336326558 -1.099719e-01 -0.870153720 -0.753491866
## asymetry coefficient length.of.kernel.groove cluster
## 1 -0.983800962 -0.38266305 1
## 2 -1.783903583 -0.91981560 1
## 3 -0.665888201 -1.18635720 1
## 4 -0.958527563 -1.22705057 1
## 5 -1.559768435 -0.47422315 1
## 6 -0.823514403 -0.91981560 1
## 7 -0.075953850 -0.38469772 1
## 8 -0.665223111 -0.83029017 1
## 9 -1.104182155 0.95411430 2
## 10 -1.151403506 0.25418826 1
## 11 0.560536763 -0.19140419 2
## 12 -1.319006050 -0.82825550 1
## 13 0.190081934 -1.36337338 1
## 14 -0.375244107 -1.09072777 3
## 15 -0.510922356 -1.18635720 3
## 16 0.322434737 -1.27588262 1
## 17 1.020113580 -1.27588262 1
## 18 -1.397486606 -0.73669541 1
## 19 -1.285751577 -1.54445890 1
## 20 0.267232312 -1.00527168 3
## 21 -0.417809832 -0.47218848 1
## 22 -0.673204185 -0.38469772 1
## 23 -1.952104707 -0.64513532 1
## 24 -1.519863067 -0.90964225 1
## 25 -1.269789430 -0.82825550 1
## 26 -1.860388871 -0.20564687 1
## 27 -0.217617905 -1.18635720 3
## 28 -0.795580645 -1.09683178 3
## 29 -0.629308280 -0.75297276 1
## 30 -0.112533770 -0.63292731 3
## 31 -1.892246656 -0.71634872 1
## 32 -0.191679416 -0.36638570 2
## 33 0.146186030 -0.22192422 2
## 34 -1.048314640 -0.80587415 1
## 35 -1.044989193 -0.09780943 1
## 36 -0.951211579 0.07106807 1
## 37 -0.583417108 0.24198024 2
## 38 -0.494960209 0.15448949 1
## 39 -0.391206254 -0.20157754 1
## 40 1.985158386 -0.82825550 1
## 41 -0.740378220 -0.46811914 1
## 42 -0.965178458 -0.47218848 1
## 43 -0.824179492 -1.27181329 1
## 44 0.672271793 0.24401491 2
## 45 -0.380564822 -0.46404980 1
## 46 -1.423425095 -0.90964225 1
## 47 -1.551787361 -0.56171390 1
## 48 -0.493630031 -0.47422315 1
## 49 -0.662562753 -0.60444194 1
## 50 -1.044989193 -0.11612145 1
## 51 0.182765950 -0.53729788 1
## 52 1.258880696 -0.55357523 1
## 53 0.276543564 -0.02456136 2
## 54 -0.247546930 -0.37452438 1
## 55 -1.475967163 0.16059350 1
## 56 -1.175346727 0.06292940 1
## 57 -0.597383986 -0.74076475 1
## 58 -1.701432490 -0.65123933 1
## 59 -1.131450823 -0.37859372 1
## 60 -1.462000284 -1.80896583 1
## 61 -0.665223111 -1.62991498 1
## 62 -0.951876668 -1.43458678 1
## 63 -0.319376592 -1.63398432 1
## 64 0.303812232 -0.65123933 3
## 65 -1.678819448 -1.27384795 1
## 66 -0.896674243 -1.62991498 1
## 67 -1.587702192 -0.52508986 1
## 68 -0.986461320 -0.56171390 1
## 69 -1.487273684 -0.21988956 1
## 70 -0.111203591 -0.69396737 3
## 71 0.249939986 1.32645867 2
## 72 0.648328572 0.95411430 2
## 73 0.557876406 0.77913279 2
## 74 -0.508261999 1.36511738 2
## 75 0.202053544 0.88086622 2
## 76 0.811275490 0.78727146 2
## 77 0.082337442 1.04567439 2
## 78 0.499348533 2.12201415 2
## 79 0.907048372 1.94092863 2
## 80 -0.560138977 0.68757270 2
## 81 1.218310239 0.96021830 2
## 82 1.079971631 0.95818364 2
## 83 0.979543123 1.58486160 2
## 84 -1.481952968 1.75984311 2
## 85 -0.490969673 1.58079227 2
## 86 -0.836151102 1.60520829 2
## 87 -1.364232133 1.42615744 2
## 88 -0.006119456 2.21764358 2
## 89 1.383252425 1.67438703 2
## 90 0.875124078 1.85750721 2
## 91 -1.160714759 2.11794481 2
## 92 -0.385885538 1.31221599 2
## 93 -0.308070071 1.31221599 2
## 94 1.530237196 0.95411430 2
## 95 0.819921653 2.11591014 2
## 96 -0.002794009 1.13723448 2
## 97 -0.148448601 1.68862971 2
## 98 -1.035012851 2.12608348 2
## 99 -0.563464424 1.75984311 2
## 100 -1.005748915 1.40174142 2
## 101 0.343717599 0.42713510 2
## 102 -1.085559650 0.87272755 2
## 103 0.404240740 1.22269057 2
## 104 -0.228259336 1.67031769 2
## 105 -0.220943352 1.50550952 2
## 106 -0.762991262 0.95818364 2
## 107 -0.570115318 1.61131230 2
## 108 0.031125553 1.05991707 2
## 109 -0.298093729 2.32344635 2
## 110 -1.305039172 0.98870367 2
## 111 -0.974489710 0.78523679 2
## 112 -0.014765619 1.13316515 2
## 113 -1.058290982 1.04974373 2
## 114 1.983163118 1.31221599 2
## 115 0.649658751 1.84733387 2
## 116 -0.965843547 1.53602955 2
## 117 0.421533066 0.69571137 2
## 118 -0.409828759 1.58079227 2
## 119 -0.040704108 1.13519981 2
## 120 -0.423795637 1.85547254 2
## 121 1.463728250 1.58689627 2
## 122 -0.054005898 1.22675991 2
## 123 0.389608772 0.60008194 2
## 124 -0.476337705 1.01108502 2
## 125 -0.242226215 -0.53729788 1
## 126 0.324430005 1.18810120 2
## 127 0.459443165 1.41191476 2
## 128 -0.959857742 1.03957038 2
## 129 -1.190643784 1.58079227 2
## 130 1.107905389 0.51462585 2
## 131 -0.574105855 1.12706114 2
## 132 -0.526884504 1.10061045 2
## 133 0.506664517 0.78727146 2
## 134 0.376306983 0.78727146 2
## 135 0.845860142 0.89307424 2
## 136 -0.066642597 0.06292940 1
## 137 -0.115859217 1.14537316 2
## 138 -0.705128479 0.95818364 2
## 139 -0.648595875 0.69978071 2
## 140 0.045757521 1.04567439 2
## 141 1.066669842 -0.02659603 3
## 142 2.217939697 0.06496407 3
## 143 1.526246659 -0.20564687 3
## 144 1.176409603 -0.38062838 3
## 145 0.512650322 -0.46811914 3
## 146 1.641972225 -0.27075627 3
## 147 -0.983800962 -0.56171390 3
## 148 0.479395849 -0.82622084 3
## 149 -0.292773014 -0.18733486 3
## 150 1.171753977 -0.43556444 3
## 151 0.994175091 -0.20564687 3
## 152 2.189340851 -0.28092962 3
## 153 0.702200818 -0.09780943 3
## 154 -0.244886573 -0.82825550 3
## 155 0.231317481 -0.29517230 3
## 156 1.405200377 -0.38469772 3
## 157 -0.234910231 -0.82418617 3
## 158 0.748091991 -0.38266305 3
## 159 0.450797002 -0.19954287 3
## 160 1.122537357 -0.19954287 3
## 161 -0.411158938 0.16873217 3
## 162 0.379632430 -0.20361221 3
## 163 0.856501573 -0.73669541 3
## 164 0.478065670 -0.47218848 3
## 165 1.787626817 -0.73059141 3
## 166 -0.997102752 -0.71634872 1
## 167 0.813935848 -0.28092962 3
## 168 -0.041369198 -0.14257214 3
## 169 0.423528335 -0.56171390 3
## 170 -0.119184665 -0.65123933 3
## 171 2.018412859 -0.49863917 3
## 172 2.005111070 -0.91981560 3
## 173 0.404905830 -0.83029017 3
## 174 1.255555249 -0.64920466 3
## 175 0.985528928 -0.45387646 3
## 176 0.713507339 -0.70210604 3
## 177 1.087287616 -0.64310065 3
## 178 0.666285987 -0.90557292 3
## 179 1.447101013 -0.82622084 3
## 180 -1.356251060 -0.46811914 3
## 181 0.835883800 -1.18635720 3
## 182 0.855836484 -0.53119387 3
## 183 0.769374854 -0.50881251 3
## 184 1.003486344 -0.55560990 3
## 185 1.652613656 -0.18733486 3
## 186 0.240628733 -0.45998047 3
## 187 0.796643522 -0.11408678 3
## 188 0.318444200 -0.91981560 3
## 189 2.543168443 -0.91778093 3
## 190 0.847855410 -1.24943193 3
## 191 1.129188252 -0.73873008 3
## 192 0.189416845 -0.82825550 3
## 193 -0.068637866 -0.56171390 3
## 194 0.766714496 -0.64920466 3
## 195 0.287184995 -0.80587415 3
## 196 0.780016285 -1.00527168 1
## 197 1.185720856 -0.91574626 1
## 198 0.645003125 -0.64513532 1
## 199 -0.262178898 -0.36028170 3
## 200 -0.580091660 -1.17618386 1
## 201 1.178404872 -0.73873008 3
## 202 -0.931258895 -1.34913070 1
## 203 0.233312749 -1.18025319 3
## 204 3.163031820 -0.83029017 1
## 205 0.145520940 -0.82825550 3
## 206 -0.046024824 -1.09479711 3
## 207 0.415547261 -0.82418617 3
## 208 3.069254206 -0.71634872 1
## 209 -0.067972776 -0.74076475 3
## 210 1.288144632 -0.70210604 3count(seeds_df_cl,cluster)## cluster n
## 1 1 65
## 2 2 75
## 3 3 70#Features and clusters
library(ggplot2)
ggplot(seeds_df_cl, aes(x=area, y = perimeter, color = factor(cluster))) + geom_point()
#Validating the reults: Using the Dunn's Index
library("clValid")## Loading required package: clusterdunn(dist_mat,cut_avg)## [1] 0.09967099set.seed(1234)
data(iris)
ir3 <- kmeans(iris[,-5],center = 3, iter.max = 200)
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"table(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] 0.1066667R Markdown
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