library(ade4)
library(gclus)
## Carregando pacotes exigidos: cluster
library(cluster)
library(RColorBrewer)
library(tidyverse)
## ── Attaching packages
## ───────────────────────────────────────
## tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.1
## ✔ readr 2.1.2 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
library(factoextra)
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
load("C:/Users/vahum/AppData/Local/R/win-library/4.2/rmarkdown/rmarkdown/templates/github_document/NEwR-2ed_code_data/NEwR2-Data/Doubs.RData")
spe
## Cogo Satr Phph Babl Thth Teso Chna Pato Lele Sqce Baba Albi Gogo Eslu Pefl
## 1 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 5 4 3 0 0 0 0 0 0 0 0 0 0 0
## 3 0 5 5 5 0 0 0 0 0 0 0 0 0 1 0
## 4 0 4 5 5 0 0 0 0 0 1 0 0 1 2 2
## 5 0 2 3 2 0 0 0 0 5 2 0 0 2 4 4
## 6 0 3 4 5 0 0 0 0 1 2 0 0 1 1 1
## 7 0 5 4 5 0 0 0 0 1 1 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 1 3 0 0 0 0 0 5 0 0 0 0 0
## 10 0 1 4 4 0 0 0 0 2 2 0 0 1 0 0
## 11 1 3 4 1 1 0 0 0 0 1 0 0 0 0 0
## 12 2 5 4 4 2 0 0 0 0 1 0 0 0 0 0
## 13 2 5 5 2 3 2 0 0 0 0 0 0 0 0 0
## 14 3 5 5 4 4 3 0 0 0 1 1 0 1 1 0
## 15 3 4 4 5 2 4 0 0 3 3 2 0 2 0 0
## 16 2 3 3 5 0 5 0 4 5 2 2 1 2 1 1
## 17 1 2 4 4 1 2 1 4 3 2 3 4 1 1 2
## 18 1 1 3 3 1 1 1 3 2 3 3 3 2 1 3
## 19 0 0 3 5 0 1 2 3 2 1 2 2 4 1 1
## 20 0 0 1 2 0 0 2 2 2 3 4 3 4 2 2
## 21 0 0 1 1 0 0 2 2 2 2 4 2 5 3 3
## 22 0 0 0 1 0 0 3 2 3 4 5 1 5 3 4
## 23 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 24 0 0 0 0 0 0 1 0 0 2 0 0 1 0 0
## 25 0 0 0 0 0 0 0 0 1 1 0 0 2 1 0
## 26 0 0 0 1 0 0 1 0 1 2 2 1 3 2 1
## 27 0 0 0 1 0 0 1 1 2 3 4 1 4 4 1
## 28 0 0 0 1 0 0 1 1 2 4 3 1 4 3 2
## 29 0 1 1 1 1 1 2 2 3 4 5 3 5 5 4
## 30 0 0 0 0 0 0 1 2 3 3 3 5 5 4 5
## Rham Legi Scer Cyca Titi Abbr Icme Gyce Ruru Blbj Alal Anan
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 1 0 0 0 0 0 0 0
## 5 0 0 2 0 3 0 0 0 5 0 0 0
## 6 0 0 0 0 2 0 0 0 1 0 0 0
## 7 0 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 1 0 0 0 4 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0 0 0
## 15 0 0 0 0 1 0 0 0 0 0 0 0
## 16 0 1 0 1 1 0 0 0 1 0 0 0
## 17 1 1 0 1 1 0 0 0 2 0 2 1
## 18 2 1 0 1 1 0 0 1 2 0 2 1
## 19 2 1 1 1 2 1 0 1 5 1 3 1
## 20 3 2 2 1 4 1 0 2 5 2 5 2
## 21 3 2 2 2 4 3 1 3 5 3 5 2
## 22 3 3 2 3 4 4 2 4 5 4 5 2
## 23 0 0 0 0 0 0 0 0 1 0 2 0
## 24 0 1 0 0 0 0 0 2 2 1 5 0
## 25 0 0 1 0 0 0 0 1 1 0 3 0
## 26 2 2 1 1 3 2 1 4 4 2 5 2
## 27 3 3 1 2 5 3 2 5 5 4 5 3
## 28 4 4 2 4 4 3 3 5 5 5 5 4
## 29 5 5 2 3 3 4 4 5 5 4 5 4
## 30 5 3 5 5 5 5 5 5 5 5 5 5
env
## dfs ele slo dis pH har pho nit amm oxy bod
## 1 0.3 934 48.0 0.84 7.9 45 0.01 0.20 0.00 12.2 2.7
## 2 2.2 932 3.0 1.00 8.0 40 0.02 0.20 0.10 10.3 1.9
## 3 10.2 914 3.7 1.80 8.3 52 0.05 0.22 0.05 10.5 3.5
## 4 18.5 854 3.2 2.53 8.0 72 0.10 0.21 0.00 11.0 1.3
## 5 21.5 849 2.3 2.64 8.1 84 0.38 0.52 0.20 8.0 6.2
## 6 32.4 846 3.2 2.86 7.9 60 0.20 0.15 0.00 10.2 5.3
## 7 36.8 841 6.6 4.00 8.1 88 0.07 0.15 0.00 11.1 2.2
## 8 49.1 792 2.5 1.30 8.1 94 0.20 0.41 0.12 7.0 8.1
## 9 70.5 752 1.2 4.80 8.0 90 0.30 0.82 0.12 7.2 5.2
## 10 99.0 617 9.9 10.00 7.7 82 0.06 0.75 0.01 10.0 4.3
## 11 123.4 483 4.1 19.90 8.1 96 0.30 1.60 0.00 11.5 2.7
## 12 132.4 477 1.6 20.00 7.9 86 0.04 0.50 0.00 12.2 3.0
## 13 143.6 450 2.1 21.10 8.1 98 0.06 0.52 0.00 12.4 2.4
## 14 152.2 434 1.2 21.20 8.3 98 0.27 1.23 0.00 12.3 3.8
## 15 164.5 415 0.5 23.00 8.6 86 0.40 1.00 0.00 11.7 2.1
## 16 185.9 375 2.0 16.10 8.0 88 0.20 2.00 0.05 10.3 2.7
## 17 198.5 349 0.5 24.30 8.0 92 0.20 2.50 0.20 10.2 4.6
## 18 211.0 333 0.8 25.00 8.0 90 0.50 2.20 0.20 10.3 2.8
## 19 224.6 310 0.5 25.90 8.1 84 0.60 2.20 0.15 10.6 3.3
## 20 247.7 286 0.8 26.80 8.0 86 0.30 3.00 0.30 10.3 2.8
## 21 282.1 262 1.0 27.20 7.9 85 0.20 2.20 0.10 9.0 4.1
## 22 294.0 254 1.4 27.90 8.1 88 0.20 1.62 0.07 9.1 4.8
## 23 304.3 246 1.2 28.80 8.1 97 2.60 3.50 1.15 6.3 16.4
## 24 314.7 241 0.3 29.76 8.0 99 1.40 2.50 0.60 5.2 12.3
## 25 327.8 231 0.5 38.70 7.9 100 4.22 6.20 1.80 4.1 16.7
## 26 356.9 214 0.5 39.10 7.9 94 1.43 3.00 0.30 6.2 8.9
## 27 373.2 206 1.2 39.60 8.1 90 0.58 3.00 0.26 7.2 6.3
## 28 394.7 195 0.3 43.20 8.3 100 0.74 4.00 0.30 8.1 4.5
## 29 422.0 183 0.6 67.70 7.8 110 0.45 1.62 0.10 9.0 4.2
## 30 453.0 172 0.2 69.00 8.2 109 0.65 1.60 0.10 8.2 4.4
spa
## X Y
## 1 85.678 20.000
## 2 84.955 20.100
## 3 92.301 23.796
## 4 91.280 26.431
## 5 92.005 29.163
## 6 95.954 36.315
## 7 98.201 38.799
## 8 99.455 46.406
## 9 109.782 55.865
## 10 130.641 66.576
## 11 142.748 81.258
## 12 147.270 85.839
## 13 156.817 89.516
## 14 159.435 92.791
## 15 150.820 91.084
## 16 132.662 87.956
## 17 128.298 93.918
## 18 130.560 102.446
## 19 128.459 105.428
## 20 114.862 103.129
## 21 97.163 90.245
## 22 88.200 86.373
## 23 79.596 83.508
## 24 74.753 78.734
## 25 67.146 74.683
## 26 53.770 71.598
## 27 43.637 68.673
## 28 30.514 61.166
## 29 20.495 43.848
## 30 0.000 41.562
1)Use a base “spe” e tente econtrar grupos de amostras (comunidades)
que pertencem à trechos específicos do rio
k3<-kmeans(spe, centers = 3, nstart=25)
k3
## K-means clustering with 3 clusters of sizes 12, 8, 10
##
## Cluster means:
## Cogo Satr Phph Babl Thth Teso Chna Pato Lele Sqce
## 1 1.083333 4.000 4.250 4.0 1.000 1.166667 0.000 0.3333333 1.00 1.166667
## 2 0.000000 0.125 0.375 1.0 0.125 0.125000 1.625 1.5000000 2.25 3.125000
## 3 0.200000 0.800 1.400 1.7 0.200 0.400000 0.500 1.0000000 1.30 1.700000
## Baba Albi Gogo Eslu Pefl Rham Legi Scer
## 1 0.4166667 0.08333333 0.6666667 0.50 0.3333333 0.0 0.08333333 0.000
## 2 3.7500000 2.12500000 4.3750000 3.25 2.7500000 3.5 3.00000000 2.125
## 3 0.8000000 0.90000000 1.2000000 0.80 1.0000000 0.5 0.40000000 0.400
## Cyca Titi Abbr Icme Gyce Ruru Blbj Alal Anan
## 1 0.08333333 0.4166667 0.000 0.00 0.000 0.1666667 0.000 0.0 0.0
## 2 2.62500000 4.0000000 3.125 2.25 4.125 4.8750000 3.625 5.0 3.0
## 3 0.30000000 0.8000000 0.100 0.00 0.500 2.2000000 0.200 1.7 0.3
##
## Clustering vector:
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
## 3 1 1 1 3 1 1 3 3 1 1 1 1 1 1 1 3 3 3 2 2 2 3 3 3 2
## 27 28 29 30
## 2 2 2 2
##
## Within cluster sum of squares by cluster:
## [1] 203.75 176.75 329.90
## (between_SS / total_SS = 64.2 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"
2)teste tanto medidas de distância quanto métodos de agrupamento
diferentes
Aglomerativo
spe
## Cogo Satr Phph Babl Thth Teso Chna Pato Lele Sqce Baba Albi Gogo Eslu Pefl
## 1 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 5 4 3 0 0 0 0 0 0 0 0 0 0 0
## 3 0 5 5 5 0 0 0 0 0 0 0 0 0 1 0
## 4 0 4 5 5 0 0 0 0 0 1 0 0 1 2 2
## 5 0 2 3 2 0 0 0 0 5 2 0 0 2 4 4
## 6 0 3 4 5 0 0 0 0 1 2 0 0 1 1 1
## 7 0 5 4 5 0 0 0 0 1 1 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 1 3 0 0 0 0 0 5 0 0 0 0 0
## 10 0 1 4 4 0 0 0 0 2 2 0 0 1 0 0
## 11 1 3 4 1 1 0 0 0 0 1 0 0 0 0 0
## 12 2 5 4 4 2 0 0 0 0 1 0 0 0 0 0
## 13 2 5 5 2 3 2 0 0 0 0 0 0 0 0 0
## 14 3 5 5 4 4 3 0 0 0 1 1 0 1 1 0
## 15 3 4 4 5 2 4 0 0 3 3 2 0 2 0 0
## 16 2 3 3 5 0 5 0 4 5 2 2 1 2 1 1
## 17 1 2 4 4 1 2 1 4 3 2 3 4 1 1 2
## 18 1 1 3 3 1 1 1 3 2 3 3 3 2 1 3
## 19 0 0 3 5 0 1 2 3 2 1 2 2 4 1 1
## 20 0 0 1 2 0 0 2 2 2 3 4 3 4 2 2
## 21 0 0 1 1 0 0 2 2 2 2 4 2 5 3 3
## 22 0 0 0 1 0 0 3 2 3 4 5 1 5 3 4
## 23 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## 24 0 0 0 0 0 0 1 0 0 2 0 0 1 0 0
## 25 0 0 0 0 0 0 0 0 1 1 0 0 2 1 0
## 26 0 0 0 1 0 0 1 0 1 2 2 1 3 2 1
## 27 0 0 0 1 0 0 1 1 2 3 4 1 4 4 1
## 28 0 0 0 1 0 0 1 1 2 4 3 1 4 3 2
## 29 0 1 1 1 1 1 2 2 3 4 5 3 5 5 4
## 30 0 0 0 0 0 0 1 2 3 3 3 5 5 4 5
## Rham Legi Scer Cyca Titi Abbr Icme Gyce Ruru Blbj Alal Anan
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 1 0 0 0 0 0 0 0
## 5 0 0 2 0 3 0 0 0 5 0 0 0
## 6 0 0 0 0 2 0 0 0 1 0 0 0
## 7 0 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 1 0 0 0 4 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0 0 0
## 15 0 0 0 0 1 0 0 0 0 0 0 0
## 16 0 1 0 1 1 0 0 0 1 0 0 0
## 17 1 1 0 1 1 0 0 0 2 0 2 1
## 18 2 1 0 1 1 0 0 1 2 0 2 1
## 19 2 1 1 1 2 1 0 1 5 1 3 1
## 20 3 2 2 1 4 1 0 2 5 2 5 2
## 21 3 2 2 2 4 3 1 3 5 3 5 2
## 22 3 3 2 3 4 4 2 4 5 4 5 2
## 23 0 0 0 0 0 0 0 0 1 0 2 0
## 24 0 1 0 0 0 0 0 2 2 1 5 0
## 25 0 0 1 0 0 0 0 1 1 0 3 0
## 26 2 2 1 1 3 2 1 4 4 2 5 2
## 27 3 3 1 2 5 3 2 5 5 4 5 3
## 28 4 4 2 4 4 3 3 5 5 5 5 4
## 29 5 5 2 3 3 4 4 5 5 4 5 4
## 30 5 3 5 5 5 5 5 5 5 5 5 5
df <- spe
df <- na.omit(df)
df <- scale(df)
head(df)
## Cogo Satr Phph Babl Thth Teso Chna
## 1 -0.5332108 0.53918531 -1.1439008 -1.2646370 -0.4957446 -0.487395 -0.7017498
## 2 -0.5332108 1.51952223 0.8747476 0.2945045 -0.4957446 -0.487395 -0.7017498
## 3 -0.5332108 1.51952223 1.3794098 1.3339321 -0.4957446 -0.487395 -0.7017498
## 4 -0.5332108 1.02935377 1.3794098 1.3339321 -0.4957446 -0.487395 -0.7017498
## 5 -0.5332108 0.04901685 0.3700855 -0.2252093 -0.4957446 -0.487395 -0.7017498
## 6 -0.5332108 0.53918531 0.8747476 1.3339321 -0.4957446 -0.487395 -0.7017498
## Pato Lele Sqce Baba Albi Gogo
## 1 -0.6635823 -0.9547024 -1.37475155 -0.8165042 -0.6436503 -0.99645665
## 2 -0.6635823 -0.9547024 -1.37475155 -0.8165042 -0.6436503 -0.99645665
## 3 -0.6635823 -0.9547024 -1.37475155 -0.8165042 -0.6436503 -0.99645665
## 4 -0.6635823 -0.9547024 -0.63827750 -0.8165042 -0.6436503 -0.45293484
## 5 -0.6635823 2.3756549 0.09819654 -0.8165042 -0.6436503 0.09058697
## 6 -0.6635823 -0.2886310 0.09819654 -0.8165042 -0.6436503 -0.45293484
## Eslu Pefl Rham Legi Scer Cyca Titi
## 1 -0.8793937 -0.7790871 -0.6677353 -0.6897081 -0.6091127 -0.6213284 -0.8635475
## 2 -0.8793937 -0.7790871 -0.6677353 -0.6897081 -0.6091127 -0.6213284 -0.8635475
## 3 -0.2198484 -0.7790871 -0.6677353 -0.6897081 -0.6091127 -0.6213284 -0.8635475
## 4 0.4396969 0.5193914 -0.6677353 -0.6897081 -0.6091127 -0.6213284 -0.2878492
## 5 1.7587875 1.8178700 -0.6677353 -0.6897081 1.1312093 -0.6213284 0.8635475
## 6 -0.2198484 -0.1298479 -0.6677353 -0.6897081 -0.6091127 -0.6213284 0.2878492
## Abbr Icme Gyce Ruru Blbj Alal Anan
## 1 -0.5682069 -0.4606464 -0.6693037 -0.9533914 -0.6036916 -0.8447682 -0.6220813
## 2 -0.5682069 -0.4606464 -0.6693037 -0.9533914 -0.6036916 -0.8447682 -0.6220813
## 3 -0.5682069 -0.4606464 -0.6693037 -0.9533914 -0.6036916 -0.8447682 -0.6220813
## 4 -0.5682069 -0.4606464 -0.6693037 -0.9533914 -0.6036916 -0.8447682 -0.6220813
## 5 -0.5682069 -0.4606464 -0.6693037 1.3165882 -0.6036916 -0.8447682 -0.6220813
## 6 -0.5682069 -0.4606464 -0.6693037 -0.4993955 -0.6036916 -0.8447682 -0.6220813
d <- dist(df, method = "euclidean")
hc1 <- hclust(d, method = "complete" )
plot(hc1, cex = 0.6, hang = -1)
grup_spe<-cutree(hc1, k=12)
table(grup_spe)
## grup_spe
## 1 2 3 4 5 6 7 8 9 10 11 12
## 5 7 1 1 3 1 1 3 3 3 1 1
plot(hc1)
rect.hclust(hc1, k = 12, border = 2:12)
Divisivo
hc4 <- diana(df)
hc4$dc
## [1] 0.792261
pltree(hc4, cex = 0.6, hang = -1, main = "Dendrogram of diana")
grup_spee<-cutree(hc4, k=6)
table(grup_spee)
## grup_spee
## 1 2 3 4 5 6
## 13 1 3 5 6 2
plot(hc4)
rect.hclust(hc4, k = 6, border = 2:6)
3)Use métodos de K-means para encontrar quantos clusters há de fato
segundo o método “silhouette”
fviz_cluster(k3, data = spe)
fviz_nbclust(spe, kmeans, method = "silhouette")
k2<-kmeans(spe, centers =2, nstart=25)
fviz_cluster(k2, data = spe)
4)Garafique o rio colorindo as amostras segundo seu pertencimento
aos clusters gerados como esse aqui
k2<-kmeans(spa, centers = 2, nstart=25)
k2
## K-means clustering with 2 clusters of sizes 18, 12
##
## Cluster means:
## X Y
## 1 72.65122 50.39000
## 2 134.97792 90.84883
##
## Clustering vector:
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
## 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1
## 27 28 29 30
## 1 1 1 1
##
## Within cluster sum of squares by cluster:
## [1] 24776.02 4730.82
## (between_SS / total_SS = 57.4 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"
fviz_cluster(k2, data = spa)
As características ph, pho, oxy e bod levam a clara separaçõa de 2
áreas no rio.
e3<-kmeans(env[,-c(1,4,5,7,10,11,12)], centers = 3, nstart=25)
e3
## K-means clustering with 3 clusters of sizes 14, 9, 7
##
## Cluster means:
## ele slo har nit amm
## 1 248.7143 0.700000 94.57143 2.795714 0.402142857
## 2 857.1111 8.188889 69.44444 0.320000 0.065555556
## 3 464.4286 3.057143 90.57143 1.085714 0.008571429
##
## Clustering vector:
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
## 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1
## 27 28 29 30
## 1 1 1 1
##
## Within cluster sum of squares by cluster:
## [1] 39934.35 35749.75 35681.39
## (between_SS / total_SS = 94.8 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"
fviz_cluster(e3, data = env[,-c(1,4,5,7,10,11,12)])
O gráfico está meio confuso portanto irei confirmar se 3 é o melhor
número de clusters para separar.
fviz_nbclust(env[,-c(1,4,5,7,10,11,12)], kmeans, method = "silhouette")
O resultado mostra que 2 clusters é a melhor opção para esses
dados.
e2.2<-kmeans(env[,-c(1,4,5,7,10,11,12)], centers = 2, nstart=25)
e2.2
## K-means clustering with 2 clusters of sizes 10, 20
##
## Cluster means:
## ele slo har nit amm
## 1 833.1 8.360 70.7 0.3630 0.060
## 2 305.8 1.065 93.8 2.2995 0.284
##
## Clustering vector:
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
## 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 27 28 29 30
## 2 2 2 2
##
## Within cluster sum of squares by cluster:
## [1] 87782.44 200440.03
## (between_SS / total_SS = 86.6 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"
fviz_cluster(e2.2, data = env[,-c(1,4,5,7,10,11,12)])
Portanto, as caracteristica ele, slo, har, nit, e amn geraram um
melhor resultado com 2 clusters,e consquentemente, mostra a relação
entre os dados biologicos analisados no primeiro e segundo gráfico.
