Segmentación de vinos
Valeria Nango - A01174106
2024-08-12
Paso 1. Instalar paquetes y llamar librerías
#install.packages("cluster")
library(cluster)
#install.packages("ggplot2")
library(ggplot2)
#install.packages("data.table")
library(data.table)
#install.packages("factoextra")
library(factoextra)## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBaPaso 2. Obtener los datos
datos <- read.csv("C:/Users/valer/OneDrive/Escritorio/IA con impacto empresarial/Base de datos/wine.csv")
str(datos)## 'data.frame': 178 obs. of 13 variables:
## $ Alcohol : num 14.2 13.2 13.2 14.4 13.2 ...
## $ Malic_Acid : num 1.71 1.78 2.36 1.95 2.59 1.76 1.87 2.15 1.64 1.35 ...
## $ Ash : num 2.43 2.14 2.67 2.5 2.87 2.45 2.45 2.61 2.17 2.27 ...
## $ Ash_Alcanity : num 15.6 11.2 18.6 16.8 21 15.2 14.6 17.6 14 16 ...
## $ Magnesium : int 127 100 101 113 118 112 96 121 97 98 ...
## $ Total_Phenols : num 2.8 2.65 2.8 3.85 2.8 3.27 2.5 2.6 2.8 2.98 ...
## $ Flavanoids : num 3.06 2.76 3.24 3.49 2.69 3.39 2.52 2.51 2.98 3.15 ...
## $ Nonflavanoid_Phenols: num 0.28 0.26 0.3 0.24 0.39 0.34 0.3 0.31 0.29 0.22 ...
## $ Proanthocyanins : num 2.29 1.28 2.81 2.18 1.82 1.97 1.98 1.25 1.98 1.85 ...
## $ Color_Intensity : num 5.64 4.38 5.68 7.8 4.32 6.75 5.25 5.05 5.2 7.22 ...
## $ Hue : num 1.04 1.05 1.03 0.86 1.04 1.05 1.02 1.06 1.08 1.01 ...
## $ OD280 : num 3.92 3.4 3.17 3.45 2.93 2.85 3.58 3.58 2.85 3.55 ...
## $ Proline : int 1065 1050 1185 1480 735 1450 1290 1295 1045 1045 ...Paso 3. Entender la base de datos
summary(datos)## Alcohol Malic_Acid Ash Ash_Alcanity
## Min. :11.03 Min. :0.740 Min. :1.360 Min. :10.60
## 1st Qu.:12.36 1st Qu.:1.603 1st Qu.:2.210 1st Qu.:17.20
## Median :13.05 Median :1.865 Median :2.360 Median :19.50
## Mean :13.00 Mean :2.336 Mean :2.367 Mean :19.49
## 3rd Qu.:13.68 3rd Qu.:3.083 3rd Qu.:2.558 3rd Qu.:21.50
## Max. :14.83 Max. :5.800 Max. :3.230 Max. :30.00
## Magnesium Total_Phenols Flavanoids Nonflavanoid_Phenols
## Min. : 70.00 Min. :0.980 Min. :0.340 Min. :0.1300
## 1st Qu.: 88.00 1st Qu.:1.742 1st Qu.:1.205 1st Qu.:0.2700
## Median : 98.00 Median :2.355 Median :2.135 Median :0.3400
## Mean : 99.74 Mean :2.295 Mean :2.029 Mean :0.3619
## 3rd Qu.:107.00 3rd Qu.:2.800 3rd Qu.:2.875 3rd Qu.:0.4375
## Max. :162.00 Max. :3.880 Max. :5.080 Max. :0.6600
## Proanthocyanins Color_Intensity Hue OD280
## Min. :0.410 Min. : 1.280 Min. :0.4800 Min. :1.270
## 1st Qu.:1.250 1st Qu.: 3.220 1st Qu.:0.7825 1st Qu.:1.938
## Median :1.555 Median : 4.690 Median :0.9650 Median :2.780
## Mean :1.591 Mean : 5.058 Mean :0.9574 Mean :2.612
## 3rd Qu.:1.950 3rd Qu.: 6.200 3rd Qu.:1.1200 3rd Qu.:3.170
## Max. :3.580 Max. :13.000 Max. :1.7100 Max. :4.000
## Proline
## Min. : 278.0
## 1st Qu.: 500.5
## Median : 673.5
## Mean : 746.9
## 3rd Qu.: 985.0
## Max. :1680.0Paso 4. Escalar la base de datos
df <- scale(datos)Paso 5. Cantidad de grupos
grupos <- 3Paso 6. Generar los segmentos
segmentos <- kmeans(df,grupos)
segmentos## K-means clustering with 3 clusters of sizes 51, 62, 65
##
## Cluster means:
## Alcohol Malic_Acid Ash Ash_Alcanity Magnesium Total_Phenols
## 1 0.1644436 0.8690954 0.1863726 0.5228924 -0.07526047 -0.97657548
## 2 0.8328826 -0.3029551 0.3636801 -0.6084749 0.57596208 0.88274724
## 3 -0.9234669 -0.3929331 -0.4931257 0.1701220 -0.49032869 -0.07576891
## Flavanoids Nonflavanoid_Phenols Proanthocyanins Color_Intensity Hue
## 1 -1.21182921 0.72402116 -0.77751312 0.9388902 -1.1615122
## 2 0.97506900 -0.56050853 0.57865427 0.1705823 0.4726504
## 3 0.02075402 -0.03343924 0.05810161 -0.8993770 0.4605046
## OD280 Proline
## 1 -1.2887761 -0.4059428
## 2 0.7770551 1.1220202
## 3 0.2700025 -0.7517257
##
## Clustering vector:
## [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 1 3 3 3 3 3 3 3 3 3 3 3 2
## [75] 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [112] 3 3 3 3 3 3 3 1 3 3 2 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 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
##
## Within cluster sum of squares by cluster:
## [1] 326.3537 385.6983 558.6971
## (between_SS / total_SS = 44.8 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"Paso 7. Asignar el grupo al que pertenece cada observación
asignacion <- cbind(datos, cluster = segmentos$cluster)
asignacion## Alcohol Malic_Acid Ash Ash_Alcanity Magnesium Total_Phenols Flavanoids
## 1 14.23 1.71 2.43 15.6 127 2.80 3.06
## 2 13.20 1.78 2.14 11.2 100 2.65 2.76
## 3 13.16 2.36 2.67 18.6 101 2.80 3.24
## 4 14.37 1.95 2.50 16.8 113 3.85 3.49
## 5 13.24 2.59 2.87 21.0 118 2.80 2.69
## 6 14.20 1.76 2.45 15.2 112 3.27 3.39
## 7 14.39 1.87 2.45 14.6 96 2.50 2.52
## 8 14.06 2.15 2.61 17.6 121 2.60 2.51
## 9 14.83 1.64 2.17 14.0 97 2.80 2.98
## 10 13.86 1.35 2.27 16.0 98 2.98 3.15
## 11 14.10 2.16 2.30 18.0 105 2.95 3.32
## 12 14.12 1.48 2.32 16.8 95 2.20 2.43
## 13 13.75 1.73 2.41 16.0 89 2.60 2.76
## 14 14.75 1.73 2.39 11.4 91 3.10 3.69
## 15 14.38 1.87 2.38 12.0 102 3.30 3.64
## 16 13.63 1.81 2.70 17.2 112 2.85 2.91
## 17 14.30 1.92 2.72 20.0 120 2.80 3.14
## 18 13.83 1.57 2.62 20.0 115 2.95 3.40
## 19 14.19 1.59 2.48 16.5 108 3.30 3.93
## 20 13.64 3.10 2.56 15.2 116 2.70 3.03
## 21 14.06 1.63 2.28 16.0 126 3.00 3.17
## 22 12.93 3.80 2.65 18.6 102 2.41 2.41
## 23 13.71 1.86 2.36 16.6 101 2.61 2.88
## 24 12.85 1.60 2.52 17.8 95 2.48 2.37
## 25 13.50 1.81 2.61 20.0 96 2.53 2.61
## 26 13.05 2.05 3.22 25.0 124 2.63 2.68
## 27 13.39 1.77 2.62 16.1 93 2.85 2.94
## 28 13.30 1.72 2.14 17.0 94 2.40 2.19
## 29 13.87 1.90 2.80 19.4 107 2.95 2.97
## 30 14.02 1.68 2.21 16.0 96 2.65 2.33
## 31 13.73 1.50 2.70 22.5 101 3.00 3.25
## 32 13.58 1.66 2.36 19.1 106 2.86 3.19
## 33 13.68 1.83 2.36 17.2 104 2.42 2.69
## 34 13.76 1.53 2.70 19.5 132 2.95 2.74
## 35 13.51 1.80 2.65 19.0 110 2.35 2.53
## 36 13.48 1.81 2.41 20.5 100 2.70 2.98
## 37 13.28 1.64 2.84 15.5 110 2.60 2.68
## 38 13.05 1.65 2.55 18.0 98 2.45 2.43
## 39 13.07 1.50 2.10 15.5 98 2.40 2.64
## 40 14.22 3.99 2.51 13.2 128 3.00 3.04
## 41 13.56 1.71 2.31 16.2 117 3.15 3.29
## 42 13.41 3.84 2.12 18.8 90 2.45 2.68
## 43 13.88 1.89 2.59 15.0 101 3.25 3.56
## 44 13.24 3.98 2.29 17.5 103 2.64 2.63
## 45 13.05 1.77 2.10 17.0 107 3.00 3.00
## 46 14.21 4.04 2.44 18.9 111 2.85 2.65
## 47 14.38 3.59 2.28 16.0 102 3.25 3.17
## 48 13.90 1.68 2.12 16.0 101 3.10 3.39
## 49 14.10 2.02 2.40 18.8 103 2.75 2.92
## 50 13.94 1.73 2.27 17.4 108 2.88 3.54
## 51 13.05 1.73 2.04 12.4 92 2.72 3.27
## 52 13.83 1.65 2.60 17.2 94 2.45 2.99
## 53 13.82 1.75 2.42 14.0 111 3.88 3.74
## 54 13.77 1.90 2.68 17.1 115 3.00 2.79
## 55 13.74 1.67 2.25 16.4 118 2.60 2.90
## 56 13.56 1.73 2.46 20.5 116 2.96 2.78
## 57 14.22 1.70 2.30 16.3 118 3.20 3.00
## 58 13.29 1.97 2.68 16.8 102 3.00 3.23
## 59 13.72 1.43 2.50 16.7 108 3.40 3.67
## 60 12.37 0.94 1.36 10.6 88 1.98 0.57
## 61 12.33 1.10 2.28 16.0 101 2.05 1.09
## 62 12.64 1.36 2.02 16.8 100 2.02 1.41
## 63 13.67 1.25 1.92 18.0 94 2.10 1.79
## 64 12.37 1.13 2.16 19.0 87 3.50 3.10
## 65 12.17 1.45 2.53 19.0 104 1.89 1.75
## 66 12.37 1.21 2.56 18.1 98 2.42 2.65
## 67 13.11 1.01 1.70 15.0 78 2.98 3.18
## 68 12.37 1.17 1.92 19.6 78 2.11 2.00
## 69 13.34 0.94 2.36 17.0 110 2.53 1.30
## 70 12.21 1.19 1.75 16.8 151 1.85 1.28
## 71 12.29 1.61 2.21 20.4 103 1.10 1.02
## 72 13.86 1.51 2.67 25.0 86 2.95 2.86
## 73 13.49 1.66 2.24 24.0 87 1.88 1.84
## 74 12.99 1.67 2.60 30.0 139 3.30 2.89
## 75 11.96 1.09 2.30 21.0 101 3.38 2.14
## 76 11.66 1.88 1.92 16.0 97 1.61 1.57
## 77 13.03 0.90 1.71 16.0 86 1.95 2.03
## 78 11.84 2.89 2.23 18.0 112 1.72 1.32
## 79 12.33 0.99 1.95 14.8 136 1.90 1.85
## 80 12.70 3.87 2.40 23.0 101 2.83 2.55
## 81 12.00 0.92 2.00 19.0 86 2.42 2.26
## 82 12.72 1.81 2.20 18.8 86 2.20 2.53
## 83 12.08 1.13 2.51 24.0 78 2.00 1.58
## 84 13.05 3.86 2.32 22.5 85 1.65 1.59
## 85 11.84 0.89 2.58 18.0 94 2.20 2.21
## 86 12.67 0.98 2.24 18.0 99 2.20 1.94
## 87 12.16 1.61 2.31 22.8 90 1.78 1.69
## 88 11.65 1.67 2.62 26.0 88 1.92 1.61
## 89 11.64 2.06 2.46 21.6 84 1.95 1.69
## 90 12.08 1.33 2.30 23.6 70 2.20 1.59
## 91 12.08 1.83 2.32 18.5 81 1.60 1.50
## 92 12.00 1.51 2.42 22.0 86 1.45 1.25
## 93 12.69 1.53 2.26 20.7 80 1.38 1.46
## 94 12.29 2.83 2.22 18.0 88 2.45 2.25
## 95 11.62 1.99 2.28 18.0 98 3.02 2.26
## 96 12.47 1.52 2.20 19.0 162 2.50 2.27
## 97 11.81 2.12 2.74 21.5 134 1.60 0.99
## 98 12.29 1.41 1.98 16.0 85 2.55 2.50
## 99 12.37 1.07 2.10 18.5 88 3.52 3.75
## 100 12.29 3.17 2.21 18.0 88 2.85 2.99
## 101 12.08 2.08 1.70 17.5 97 2.23 2.17
## 102 12.60 1.34 1.90 18.5 88 1.45 1.36
## 103 12.34 2.45 2.46 21.0 98 2.56 2.11
## 104 11.82 1.72 1.88 19.5 86 2.50 1.64
## 105 12.51 1.73 1.98 20.5 85 2.20 1.92
## 106 12.42 2.55 2.27 22.0 90 1.68 1.84
## 107 12.25 1.73 2.12 19.0 80 1.65 2.03
## 108 12.72 1.75 2.28 22.5 84 1.38 1.76
## 109 12.22 1.29 1.94 19.0 92 2.36 2.04
## 110 11.61 1.35 2.70 20.0 94 2.74 2.92
## 111 11.46 3.74 1.82 19.5 107 3.18 2.58
## 112 12.52 2.43 2.17 21.0 88 2.55 2.27
## 113 11.76 2.68 2.92 20.0 103 1.75 2.03
## 114 11.41 0.74 2.50 21.0 88 2.48 2.01
## 115 12.08 1.39 2.50 22.5 84 2.56 2.29
## 116 11.03 1.51 2.20 21.5 85 2.46 2.17
## 117 11.82 1.47 1.99 20.8 86 1.98 1.60
## 118 12.42 1.61 2.19 22.5 108 2.00 2.09
## 119 12.77 3.43 1.98 16.0 80 1.63 1.25
## 120 12.00 3.43 2.00 19.0 87 2.00 1.64
## 121 11.45 2.40 2.42 20.0 96 2.90 2.79
## 122 11.56 2.05 3.23 28.5 119 3.18 5.08
## 123 12.42 4.43 2.73 26.5 102 2.20 2.13
## 124 13.05 5.80 2.13 21.5 86 2.62 2.65
## 125 11.87 4.31 2.39 21.0 82 2.86 3.03
## 126 12.07 2.16 2.17 21.0 85 2.60 2.65
## 127 12.43 1.53 2.29 21.5 86 2.74 3.15
## 128 11.79 2.13 2.78 28.5 92 2.13 2.24
## 129 12.37 1.63 2.30 24.5 88 2.22 2.45
## 130 12.04 4.30 2.38 22.0 80 2.10 1.75
## 131 12.86 1.35 2.32 18.0 122 1.51 1.25
## 132 12.88 2.99 2.40 20.0 104 1.30 1.22
## 133 12.81 2.31 2.40 24.0 98 1.15 1.09
## 134 12.70 3.55 2.36 21.5 106 1.70 1.20
## 135 12.51 1.24 2.25 17.5 85 2.00 0.58
## 136 12.60 2.46 2.20 18.5 94 1.62 0.66
## 137 12.25 4.72 2.54 21.0 89 1.38 0.47
## 138 12.53 5.51 2.64 25.0 96 1.79 0.60
## 139 13.49 3.59 2.19 19.5 88 1.62 0.48
## 140 12.84 2.96 2.61 24.0 101 2.32 0.60
## 141 12.93 2.81 2.70 21.0 96 1.54 0.50
## 142 13.36 2.56 2.35 20.0 89 1.40 0.50
## 143 13.52 3.17 2.72 23.5 97 1.55 0.52
## 144 13.62 4.95 2.35 20.0 92 2.00 0.80
## 145 12.25 3.88 2.20 18.5 112 1.38 0.78
## 146 13.16 3.57 2.15 21.0 102 1.50 0.55
## 147 13.88 5.04 2.23 20.0 80 0.98 0.34
## 148 12.87 4.61 2.48 21.5 86 1.70 0.65
## 149 13.32 3.24 2.38 21.5 92 1.93 0.76
## 150 13.08 3.90 2.36 21.5 113 1.41 1.39
## 151 13.50 3.12 2.62 24.0 123 1.40 1.57
## 152 12.79 2.67 2.48 22.0 112 1.48 1.36
## 153 13.11 1.90 2.75 25.5 116 2.20 1.28
## 154 13.23 3.30 2.28 18.5 98 1.80 0.83
## 155 12.58 1.29 2.10 20.0 103 1.48 0.58
## 156 13.17 5.19 2.32 22.0 93 1.74 0.63
## 157 13.84 4.12 2.38 19.5 89 1.80 0.83
## 158 12.45 3.03 2.64 27.0 97 1.90 0.58
## 159 14.34 1.68 2.70 25.0 98 2.80 1.31
## 160 13.48 1.67 2.64 22.5 89 2.60 1.10
## 161 12.36 3.83 2.38 21.0 88 2.30 0.92
## 162 13.69 3.26 2.54 20.0 107 1.83 0.56
## 163 12.85 3.27 2.58 22.0 106 1.65 0.60
## 164 12.96 3.45 2.35 18.5 106 1.39 0.70
## 165 13.78 2.76 2.30 22.0 90 1.35 0.68
## 166 13.73 4.36 2.26 22.5 88 1.28 0.47
## 167 13.45 3.70 2.60 23.0 111 1.70 0.92
## 168 12.82 3.37 2.30 19.5 88 1.48 0.66
## 169 13.58 2.58 2.69 24.5 105 1.55 0.84
## 170 13.40 4.60 2.86 25.0 112 1.98 0.96
## 171 12.20 3.03 2.32 19.0 96 1.25 0.49
## 172 12.77 2.39 2.28 19.5 86 1.39 0.51
## 173 14.16 2.51 2.48 20.0 91 1.68 0.70
## 174 13.71 5.65 2.45 20.5 95 1.68 0.61
## 175 13.40 3.91 2.48 23.0 102 1.80 0.75
## 176 13.27 4.28 2.26 20.0 120 1.59 0.69
## 177 13.17 2.59 2.37 20.0 120 1.65 0.68
## 178 14.13 4.10 2.74 24.5 96 2.05 0.76
## Nonflavanoid_Phenols Proanthocyanins Color_Intensity Hue OD280 Proline
## 1 0.28 2.29 5.640000 1.040 3.92 1065
## 2 0.26 1.28 4.380000 1.050 3.40 1050
## 3 0.30 2.81 5.680000 1.030 3.17 1185
## 4 0.24 2.18 7.800000 0.860 3.45 1480
## 5 0.39 1.82 4.320000 1.040 2.93 735
## 6 0.34 1.97 6.750000 1.050 2.85 1450
## 7 0.30 1.98 5.250000 1.020 3.58 1290
## 8 0.31 1.25 5.050000 1.060 3.58 1295
## 9 0.29 1.98 5.200000 1.080 2.85 1045
## 10 0.22 1.85 7.220000 1.010 3.55 1045
## 11 0.22 2.38 5.750000 1.250 3.17 1510
## 12 0.26 1.57 5.000000 1.170 2.82 1280
## 13 0.29 1.81 5.600000 1.150 2.90 1320
## 14 0.43 2.81 5.400000 1.250 2.73 1150
## 15 0.29 2.96 7.500000 1.200 3.00 1547
## 16 0.30 1.46 7.300000 1.280 2.88 1310
## 17 0.33 1.97 6.200000 1.070 2.65 1280
## 18 0.40 1.72 6.600000 1.130 2.57 1130
## 19 0.32 1.86 8.700000 1.230 2.82 1680
## 20 0.17 1.66 5.100000 0.960 3.36 845
## 21 0.24 2.10 5.650000 1.090 3.71 780
## 22 0.25 1.98 4.500000 1.030 3.52 770
## 23 0.27 1.69 3.800000 1.110 4.00 1035
## 24 0.26 1.46 3.930000 1.090 3.63 1015
## 25 0.28 1.66 3.520000 1.120 3.82 845
## 26 0.47 1.92 3.580000 1.130 3.20 830
## 27 0.34 1.45 4.800000 0.920 3.22 1195
## 28 0.27 1.35 3.950000 1.020 2.77 1285
## 29 0.37 1.76 4.500000 1.250 3.40 915
## 30 0.26 1.98 4.700000 1.040 3.59 1035
## 31 0.29 2.38 5.700000 1.190 2.71 1285
## 32 0.22 1.95 6.900000 1.090 2.88 1515
## 33 0.42 1.97 3.840000 1.230 2.87 990
## 34 0.50 1.35 5.400000 1.250 3.00 1235
## 35 0.29 1.54 4.200000 1.100 2.87 1095
## 36 0.26 1.86 5.100000 1.040 3.47 920
## 37 0.34 1.36 4.600000 1.090 2.78 880
## 38 0.29 1.44 4.250000 1.120 2.51 1105
## 39 0.28 1.37 3.700000 1.180 2.69 1020
## 40 0.20 2.08 5.100000 0.890 3.53 760
## 41 0.34 2.34 6.130000 0.950 3.38 795
## 42 0.27 1.48 4.280000 0.910 3.00 1035
## 43 0.17 1.70 5.430000 0.880 3.56 1095
## 44 0.32 1.66 4.360000 0.820 3.00 680
## 45 0.28 2.03 5.040000 0.880 3.35 885
## 46 0.30 1.25 5.240000 0.870 3.33 1080
## 47 0.27 2.19 4.900000 1.040 3.44 1065
## 48 0.21 2.14 6.100000 0.910 3.33 985
## 49 0.32 2.38 6.200000 1.070 2.75 1060
## 50 0.32 2.08 8.900000 1.120 3.10 1260
## 51 0.17 2.91 7.200000 1.120 2.91 1150
## 52 0.22 2.29 5.600000 1.240 3.37 1265
## 53 0.32 1.87 7.050000 1.010 3.26 1190
## 54 0.39 1.68 6.300000 1.130 2.93 1375
## 55 0.21 1.62 5.850000 0.920 3.20 1060
## 56 0.20 2.45 6.250000 0.980 3.03 1120
## 57 0.26 2.03 6.380000 0.940 3.31 970
## 58 0.31 1.66 6.000000 1.070 2.84 1270
## 59 0.19 2.04 6.800000 0.890 2.87 1285
## 60 0.28 0.42 1.950000 1.050 1.82 520
## 61 0.63 0.41 3.270000 1.250 1.67 680
## 62 0.53 0.62 5.750000 0.980 1.59 450
## 63 0.32 0.73 3.800000 1.230 2.46 630
## 64 0.19 1.87 4.450000 1.220 2.87 420
## 65 0.45 1.03 2.950000 1.450 2.23 355
## 66 0.37 2.08 4.600000 1.190 2.30 678
## 67 0.26 2.28 5.300000 1.120 3.18 502
## 68 0.27 1.04 4.680000 1.120 3.48 510
## 69 0.55 0.42 3.170000 1.020 1.93 750
## 70 0.14 2.50 2.850000 1.280 3.07 718
## 71 0.37 1.46 3.050000 0.906 1.82 870
## 72 0.21 1.87 3.380000 1.360 3.16 410
## 73 0.27 1.03 3.740000 0.980 2.78 472
## 74 0.21 1.96 3.350000 1.310 3.50 985
## 75 0.13 1.65 3.210000 0.990 3.13 886
## 76 0.34 1.15 3.800000 1.230 2.14 428
## 77 0.24 1.46 4.600000 1.190 2.48 392
## 78 0.43 0.95 2.650000 0.960 2.52 500
## 79 0.35 2.76 3.400000 1.060 2.31 750
## 80 0.43 1.95 2.570000 1.190 3.13 463
## 81 0.30 1.43 2.500000 1.380 3.12 278
## 82 0.26 1.77 3.900000 1.160 3.14 714
## 83 0.40 1.40 2.200000 1.310 2.72 630
## 84 0.61 1.62 4.800000 0.840 2.01 515
## 85 0.22 2.35 3.050000 0.790 3.08 520
## 86 0.30 1.46 2.620000 1.230 3.16 450
## 87 0.43 1.56 2.450000 1.330 2.26 495
## 88 0.40 1.34 2.600000 1.360 3.21 562
## 89 0.48 1.35 2.800000 1.000 2.75 680
## 90 0.42 1.38 1.740000 1.070 3.21 625
## 91 0.52 1.64 2.400000 1.080 2.27 480
## 92 0.50 1.63 3.600000 1.050 2.65 450
## 93 0.58 1.62 3.050000 0.960 2.06 495
## 94 0.25 1.99 2.150000 1.150 3.30 290
## 95 0.17 1.35 3.250000 1.160 2.96 345
## 96 0.32 3.28 2.600000 1.160 2.63 937
## 97 0.14 1.56 2.500000 0.950 2.26 625
## 98 0.29 1.77 2.900000 1.230 2.74 428
## 99 0.24 1.95 4.500000 1.040 2.77 660
## 100 0.45 2.81 2.300000 1.420 2.83 406
## 101 0.26 1.40 3.300000 1.270 2.96 710
## 102 0.29 1.35 2.450000 1.040 2.77 562
## 103 0.34 1.31 2.800000 0.800 3.38 438
## 104 0.37 1.42 2.060000 0.940 2.44 415
## 105 0.32 1.48 2.940000 1.040 3.57 672
## 106 0.66 1.42 2.700000 0.860 3.30 315
## 107 0.37 1.63 3.400000 1.000 3.17 510
## 108 0.48 1.63 3.300000 0.880 2.42 488
## 109 0.39 2.08 2.700000 0.860 3.02 312
## 110 0.29 2.49 2.650000 0.960 3.26 680
## 111 0.24 3.58 2.900000 0.750 2.81 562
## 112 0.26 1.22 2.000000 0.900 2.78 325
## 113 0.60 1.05 3.800000 1.230 2.50 607
## 114 0.42 1.44 3.080000 1.100 2.31 434
## 115 0.43 1.04 2.900000 0.930 3.19 385
## 116 0.52 2.01 1.900000 1.710 2.87 407
## 117 0.30 1.53 1.950000 0.950 3.33 495
## 118 0.34 1.61 2.060000 1.060 2.96 345
## 119 0.43 0.83 3.400000 0.700 2.12 372
## 120 0.37 1.87 1.280000 0.930 3.05 564
## 121 0.32 1.83 3.250000 0.800 3.39 625
## 122 0.47 1.87 6.000000 0.930 3.69 465
## 123 0.43 1.71 2.080000 0.920 3.12 365
## 124 0.30 2.01 2.600000 0.730 3.10 380
## 125 0.21 2.91 2.800000 0.750 3.64 380
## 126 0.37 1.35 2.760000 0.860 3.28 378
## 127 0.39 1.77 3.940000 0.690 2.84 352
## 128 0.58 1.76 3.000000 0.970 2.44 466
## 129 0.40 1.90 2.120000 0.890 2.78 342
## 130 0.42 1.35 2.600000 0.790 2.57 580
## 131 0.21 0.94 4.100000 0.760 1.29 630
## 132 0.24 0.83 5.400000 0.740 1.42 530
## 133 0.27 0.83 5.700000 0.660 1.36 560
## 134 0.17 0.84 5.000000 0.780 1.29 600
## 135 0.60 1.25 5.450000 0.750 1.51 650
## 136 0.63 0.94 7.100000 0.730 1.58 695
## 137 0.53 0.80 3.850000 0.750 1.27 720
## 138 0.63 1.10 5.000000 0.820 1.69 515
## 139 0.58 0.88 5.700000 0.810 1.82 580
## 140 0.53 0.81 4.920000 0.890 2.15 590
## 141 0.53 0.75 4.600000 0.770 2.31 600
## 142 0.37 0.64 5.600000 0.700 2.47 780
## 143 0.50 0.55 4.350000 0.890 2.06 520
## 144 0.47 1.02 4.400000 0.910 2.05 550
## 145 0.29 1.14 8.210000 0.650 2.00 855
## 146 0.43 1.30 4.000000 0.600 1.68 830
## 147 0.40 0.68 4.900000 0.580 1.33 415
## 148 0.47 0.86 7.650000 0.540 1.86 625
## 149 0.45 1.25 8.420000 0.550 1.62 650
## 150 0.34 1.14 9.400000 0.570 1.33 550
## 151 0.22 1.25 8.600000 0.590 1.30 500
## 152 0.24 1.26 10.800000 0.480 1.47 480
## 153 0.26 1.56 7.100000 0.610 1.33 425
## 154 0.61 1.87 10.520000 0.560 1.51 675
## 155 0.53 1.40 7.600000 0.580 1.55 640
## 156 0.61 1.55 7.900000 0.600 1.48 725
## 157 0.48 1.56 9.010000 0.570 1.64 480
## 158 0.63 1.14 7.500000 0.670 1.73 880
## 159 0.53 2.70 13.000000 0.570 1.96 660
## 160 0.52 2.29 11.750000 0.570 1.78 620
## 161 0.50 1.04 7.650000 0.560 1.58 520
## 162 0.50 0.80 5.880000 0.960 1.82 680
## 163 0.60 0.96 5.580000 0.870 2.11 570
## 164 0.40 0.94 5.280000 0.680 1.75 675
## 165 0.41 1.03 9.580000 0.700 1.68 615
## 166 0.52 1.15 6.620000 0.780 1.75 520
## 167 0.43 1.46 10.680000 0.850 1.56 695
## 168 0.40 0.97 10.260000 0.720 1.75 685
## 169 0.39 1.54 8.660000 0.740 1.80 750
## 170 0.27 1.11 8.500000 0.670 1.92 630
## 171 0.40 0.73 5.500000 0.660 1.83 510
## 172 0.48 0.64 9.899999 0.570 1.63 470
## 173 0.44 1.24 9.700000 0.620 1.71 660
## 174 0.52 1.06 7.700000 0.640 1.74 740
## 175 0.43 1.41 7.300000 0.700 1.56 750
## 176 0.43 1.35 10.200000 0.590 1.56 835
## 177 0.53 1.46 9.300000 0.600 1.62 840
## 178 0.56 1.35 9.200000 0.610 1.60 560
## cluster
## 1 2
## 2 2
## 3 2
## 4 2
## 5 2
## 6 2
## 7 2
## 8 2
## 9 2
## 10 2
## 11 2
## 12 2
## 13 2
## 14 2
## 15 2
## 16 2
## 17 2
## 18 2
## 19 2
## 20 2
## 21 2
## 22 2
## 23 2
## 24 2
## 25 2
## 26 2
## 27 2
## 28 2
## 29 2
## 30 2
## 31 2
## 32 2
## 33 2
## 34 2
## 35 2
## 36 2
## 37 2
## 38 2
## 39 2
## 40 2
## 41 2
## 42 2
## 43 2
## 44 2
## 45 2
## 46 2
## 47 2
## 48 2
## 49 2
## 50 2
## 51 2
## 52 2
## 53 2
## 54 2
## 55 2
## 56 2
## 57 2
## 58 2
## 59 2
## 60 3
## 61 3
## 62 1
## 63 3
## 64 3
## 65 3
## 66 3
## 67 3
## 68 3
## 69 3
## 70 3
## 71 3
## 72 3
## 73 3
## 74 2
## 75 3
## 76 3
## 77 3
## 78 3
## 79 3
## 80 3
## 81 3
## 82 3
## 83 3
## 84 1
## 85 3
## 86 3
## 87 3
## 88 3
## 89 3
## 90 3
## 91 3
## 92 3
## 93 3
## 94 3
## 95 3
## 96 2
## 97 3
## 98 3
## 99 3
## 100 3
## 101 3
## 102 3
## 103 3
## 104 3
## 105 3
## 106 3
## 107 3
## 108 3
## 109 3
## 110 3
## 111 3
## 112 3
## 113 3
## 114 3
## 115 3
## 116 3
## 117 3
## 118 3
## 119 1
## 120 3
## 121 3
## 122 2
## 123 3
## 124 3
## 125 3
## 126 3
## 127 3
## 128 3
## 129 3
## 130 3
## 131 1
## 132 1
## 133 1
## 134 1
## 135 1
## 136 1
## 137 1
## 138 1
## 139 1
## 140 1
## 141 1
## 142 1
## 143 1
## 144 1
## 145 1
## 146 1
## 147 1
## 148 1
## 149 1
## 150 1
## 151 1
## 152 1
## 153 1
## 154 1
## 155 1
## 156 1
## 157 1
## 158 1
## 159 1
## 160 1
## 161 1
## 162 1
## 163 1
## 164 1
## 165 1
## 166 1
## 167 1
## 168 1
## 169 1
## 170 1
## 171 1
## 172 1
## 173 1
## 174 1
## 175 1
## 176 1
## 177 1
## 178 1Paso 8. Graficar los clusters
fviz_cluster(segmentos, data=df)
Paso 9. Optimizar la cantidad de grupos
La cantidad óptima de grupos corresponde al punto más alto de la siguiente gráfica.
set.seed(123)
optimizacion <- clusGap(df, FUN=kmeans, nstart=1, K.max =10)
plot(optimizacion, xlab="Número de clusters k")
Paso 10. Comparar segmentos
promedio <- aggregate(asignacion, by=list(asignacion$cluster), FUN=mean)
promedio## Group.1 Alcohol Malic_Acid Ash Ash_Alcanity Magnesium Total_Phenols
## 1 1 13.13412 3.307255 2.417647 21.24118 98.66667 1.683922
## 2 2 13.67677 1.997903 2.466290 17.46290 107.96774 2.847581
## 3 3 12.25092 1.897385 2.231231 20.06308 92.73846 2.247692
## Flavanoids Nonflavanoid_Phenols Proanthocyanins Color_Intensity Hue
## 1 0.8188235 0.4519608 1.145882 7.234706 0.6919608
## 2 3.0032258 0.2920968 1.922097 5.453548 1.0654839
## 3 2.0500000 0.3576923 1.624154 2.973077 1.0627077
## OD280 Proline cluster
## 1 1.696667 619.0588 1
## 2 3.163387 1100.2258 2
## 3 2.803385 510.1692 3Conclusión
La segmentación o clusters es un algoritmo útil para identificar el cultivar correspondiente a cada vino.
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