Segmentación de Vinos
Luis Angel Diaz A01662034
2024-02-19
# Leer el archivo CSV
wine_data <- read.csv("/Users/luisangel/Library/CloudStorage/OneDrive-InstitutoTecnologicoydeEstudiosSuperioresdeMonterrey/7th Season/M2/wine/wine.data", header = FALSE)
# Asignar nombres a las columnas
colnames(wine_data) <- c("Class", "Alcohol", "Malic_acid", "Ash", "Alcalinity_of_ash",
"Magnesium", "Total_phenols", "Flavanoids", "Nonflavanoid_phenols",
"Proanthocyanins", "Color_intensity", "Hue",
"OD280_OD315", "Proline")
# Ver las primeras filas del dataset
head(wine_data)## Class Alcohol Malic_acid Ash Alcalinity_of_ash Magnesium Total_phenols
## 1 1 14.23 1.71 2.43 15.6 127 2.80
## 2 1 13.20 1.78 2.14 11.2 100 2.65
## 3 1 13.16 2.36 2.67 18.6 101 2.80
## 4 1 14.37 1.95 2.50 16.8 113 3.85
## 5 1 13.24 2.59 2.87 21.0 118 2.80
## 6 1 14.20 1.76 2.45 15.2 112 3.27
## Flavanoids Nonflavanoid_phenols Proanthocyanins Color_intensity Hue
## 1 3.06 0.28 2.29 5.64 1.04
## 2 2.76 0.26 1.28 4.38 1.05
## 3 3.24 0.30 2.81 5.68 1.03
## 4 3.49 0.24 2.18 7.80 0.86
## 5 2.69 0.39 1.82 4.32 1.04
## 6 3.39 0.34 1.97 6.75 1.05
## OD280_OD315 Proline
## 1 3.92 1065
## 2 3.40 1050
## 3 3.17 1185
## 4 3.45 1480
## 5 2.93 735
## 6 2.85 1450Contexto
Estos datos son el resultado de un análisis químico de vinos cultivados en la misma región de Italia pero derivados de tres cultivares diferentes.
El análisis determinó las cantidades de 13 componentes que se encuentran en cada uno de los tres tipos de vinos.
Fuente:
Wine
dataset
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 <- wine_dataPaso 3. Entender la base de datos
summary(datos)## Class Alcohol Malic_acid Ash
## Min. :1.000 Min. :11.03 Min. :0.740 Min. :1.360
## 1st Qu.:1.000 1st Qu.:12.36 1st Qu.:1.603 1st Qu.:2.210
## Median :2.000 Median :13.05 Median :1.865 Median :2.360
## Mean :1.938 Mean :13.00 Mean :2.336 Mean :2.367
## 3rd Qu.:3.000 3rd Qu.:13.68 3rd Qu.:3.083 3rd Qu.:2.558
## Max. :3.000 Max. :14.83 Max. :5.800 Max. :3.230
## Alcalinity_of_ash Magnesium Total_phenols Flavanoids
## Min. :10.60 Min. : 70.00 Min. :0.980 Min. :0.340
## 1st Qu.:17.20 1st Qu.: 88.00 1st Qu.:1.742 1st Qu.:1.205
## Median :19.50 Median : 98.00 Median :2.355 Median :2.135
## Mean :19.49 Mean : 99.74 Mean :2.295 Mean :2.029
## 3rd Qu.:21.50 3rd Qu.:107.00 3rd Qu.:2.800 3rd Qu.:2.875
## Max. :30.00 Max. :162.00 Max. :3.880 Max. :5.080
## Nonflavanoid_phenols Proanthocyanins Color_intensity Hue
## Min. :0.1300 Min. :0.410 Min. : 1.280 Min. :0.4800
## 1st Qu.:0.2700 1st Qu.:1.250 1st Qu.: 3.220 1st Qu.:0.7825
## Median :0.3400 Median :1.555 Median : 4.690 Median :0.9650
## Mean :0.3619 Mean :1.591 Mean : 5.058 Mean :0.9574
## 3rd Qu.:0.4375 3rd Qu.:1.950 3rd Qu.: 6.200 3rd Qu.:1.1200
## Max. :0.6600 Max. :3.580 Max. :13.000 Max. :1.7100
## OD280_OD315 Proline
## Min. :1.270 Min. : 278.0
## 1st Qu.:1.938 1st Qu.: 500.5
## Median :2.780 Median : 673.5
## Mean :2.612 Mean : 746.9
## 3rd Qu.:3.170 3rd Qu.: 985.0
## Max. :4.000 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 68, 61, 49
##
## Cluster means:
## Class Alcohol Malic_acid Ash Alcalinity_of_ash Magnesium
## 1 0.07973544 -0.9195318 -0.3778231 -0.4643776 0.1750133 -0.46892793
## 2 -1.16822514 0.8756272 -0.3037196 0.3180446 -0.6626544 0.56329925
## 3 1.34366784 0.1860184 0.9024258 0.2485092 0.5820616 -0.05049296
## Total_phenols Flavanoids Nonflavanoid_phenols Proanthocyanins
## 1 -0.07372644 0.04416309 0.008736157 0.01821349
## 2 0.87403990 0.94098462 -0.583942581 0.58014642
## 3 -0.98577624 -1.23271740 0.714825281 -0.74749896
## Color_intensity Hue OD280_OD315 Proline
## 1 -0.8598525 0.4233092 0.2490794 -0.7630972
## 2 0.1667181 0.4823674 0.7648958 1.1550888
## 3 0.9857177 -1.1879477 -1.2978785 -0.3789756
##
## 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 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2
## [75] 1 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [149] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
##
## Within cluster sum of squares by cluster:
## [1] 623.1702 350.5475 304.6223
## (between_SS / total_SS = 48.4 %)
##
## 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## Class Alcohol Malic_acid Ash Alcalinity_of_ash Magnesium Total_phenols
## 1 1 14.23 1.71 2.43 15.6 127 2.80
## 2 1 13.20 1.78 2.14 11.2 100 2.65
## 3 1 13.16 2.36 2.67 18.6 101 2.80
## 4 1 14.37 1.95 2.50 16.8 113 3.85
## 5 1 13.24 2.59 2.87 21.0 118 2.80
## 6 1 14.20 1.76 2.45 15.2 112 3.27
## 7 1 14.39 1.87 2.45 14.6 96 2.50
## 8 1 14.06 2.15 2.61 17.6 121 2.60
## 9 1 14.83 1.64 2.17 14.0 97 2.80
## 10 1 13.86 1.35 2.27 16.0 98 2.98
## 11 1 14.10 2.16 2.30 18.0 105 2.95
## 12 1 14.12 1.48 2.32 16.8 95 2.20
## 13 1 13.75 1.73 2.41 16.0 89 2.60
## 14 1 14.75 1.73 2.39 11.4 91 3.10
## 15 1 14.38 1.87 2.38 12.0 102 3.30
## 16 1 13.63 1.81 2.70 17.2 112 2.85
## 17 1 14.30 1.92 2.72 20.0 120 2.80
## 18 1 13.83 1.57 2.62 20.0 115 2.95
## 19 1 14.19 1.59 2.48 16.5 108 3.30
## 20 1 13.64 3.10 2.56 15.2 116 2.70
## 21 1 14.06 1.63 2.28 16.0 126 3.00
## 22 1 12.93 3.80 2.65 18.6 102 2.41
## 23 1 13.71 1.86 2.36 16.6 101 2.61
## 24 1 12.85 1.60 2.52 17.8 95 2.48
## 25 1 13.50 1.81 2.61 20.0 96 2.53
## 26 1 13.05 2.05 3.22 25.0 124 2.63
## 27 1 13.39 1.77 2.62 16.1 93 2.85
## 28 1 13.30 1.72 2.14 17.0 94 2.40
## 29 1 13.87 1.90 2.80 19.4 107 2.95
## 30 1 14.02 1.68 2.21 16.0 96 2.65
## 31 1 13.73 1.50 2.70 22.5 101 3.00
## 32 1 13.58 1.66 2.36 19.1 106 2.86
## 33 1 13.68 1.83 2.36 17.2 104 2.42
## 34 1 13.76 1.53 2.70 19.5 132 2.95
## 35 1 13.51 1.80 2.65 19.0 110 2.35
## 36 1 13.48 1.81 2.41 20.5 100 2.70
## 37 1 13.28 1.64 2.84 15.5 110 2.60
## 38 1 13.05 1.65 2.55 18.0 98 2.45
## 39 1 13.07 1.50 2.10 15.5 98 2.40
## 40 1 14.22 3.99 2.51 13.2 128 3.00
## 41 1 13.56 1.71 2.31 16.2 117 3.15
## 42 1 13.41 3.84 2.12 18.8 90 2.45
## 43 1 13.88 1.89 2.59 15.0 101 3.25
## 44 1 13.24 3.98 2.29 17.5 103 2.64
## 45 1 13.05 1.77 2.10 17.0 107 3.00
## 46 1 14.21 4.04 2.44 18.9 111 2.85
## 47 1 14.38 3.59 2.28 16.0 102 3.25
## 48 1 13.90 1.68 2.12 16.0 101 3.10
## 49 1 14.10 2.02 2.40 18.8 103 2.75
## 50 1 13.94 1.73 2.27 17.4 108 2.88
## 51 1 13.05 1.73 2.04 12.4 92 2.72
## 52 1 13.83 1.65 2.60 17.2 94 2.45
## 53 1 13.82 1.75 2.42 14.0 111 3.88
## 54 1 13.77 1.90 2.68 17.1 115 3.00
## 55 1 13.74 1.67 2.25 16.4 118 2.60
## 56 1 13.56 1.73 2.46 20.5 116 2.96
## 57 1 14.22 1.70 2.30 16.3 118 3.20
## 58 1 13.29 1.97 2.68 16.8 102 3.00
## 59 1 13.72 1.43 2.50 16.7 108 3.40
## 60 2 12.37 0.94 1.36 10.6 88 1.98
## 61 2 12.33 1.10 2.28 16.0 101 2.05
## 62 2 12.64 1.36 2.02 16.8 100 2.02
## 63 2 13.67 1.25 1.92 18.0 94 2.10
## 64 2 12.37 1.13 2.16 19.0 87 3.50
## 65 2 12.17 1.45 2.53 19.0 104 1.89
## 66 2 12.37 1.21 2.56 18.1 98 2.42
## 67 2 13.11 1.01 1.70 15.0 78 2.98
## 68 2 12.37 1.17 1.92 19.6 78 2.11
## 69 2 13.34 0.94 2.36 17.0 110 2.53
## 70 2 12.21 1.19 1.75 16.8 151 1.85
## 71 2 12.29 1.61 2.21 20.4 103 1.10
## 72 2 13.86 1.51 2.67 25.0 86 2.95
## 73 2 13.49 1.66 2.24 24.0 87 1.88
## 74 2 12.99 1.67 2.60 30.0 139 3.30
## 75 2 11.96 1.09 2.30 21.0 101 3.38
## 76 2 11.66 1.88 1.92 16.0 97 1.61
## 77 2 13.03 0.90 1.71 16.0 86 1.95
## 78 2 11.84 2.89 2.23 18.0 112 1.72
## 79 2 12.33 0.99 1.95 14.8 136 1.90
## 80 2 12.70 3.87 2.40 23.0 101 2.83
## 81 2 12.00 0.92 2.00 19.0 86 2.42
## 82 2 12.72 1.81 2.20 18.8 86 2.20
## 83 2 12.08 1.13 2.51 24.0 78 2.00
## 84 2 13.05 3.86 2.32 22.5 85 1.65
## 85 2 11.84 0.89 2.58 18.0 94 2.20
## 86 2 12.67 0.98 2.24 18.0 99 2.20
## 87 2 12.16 1.61 2.31 22.8 90 1.78
## 88 2 11.65 1.67 2.62 26.0 88 1.92
## 89 2 11.64 2.06 2.46 21.6 84 1.95
## 90 2 12.08 1.33 2.30 23.6 70 2.20
## 91 2 12.08 1.83 2.32 18.5 81 1.60
## 92 2 12.00 1.51 2.42 22.0 86 1.45
## 93 2 12.69 1.53 2.26 20.7 80 1.38
## 94 2 12.29 2.83 2.22 18.0 88 2.45
## 95 2 11.62 1.99 2.28 18.0 98 3.02
## 96 2 12.47 1.52 2.20 19.0 162 2.50
## 97 2 11.81 2.12 2.74 21.5 134 1.60
## 98 2 12.29 1.41 1.98 16.0 85 2.55
## 99 2 12.37 1.07 2.10 18.5 88 3.52
## 100 2 12.29 3.17 2.21 18.0 88 2.85
## 101 2 12.08 2.08 1.70 17.5 97 2.23
## 102 2 12.60 1.34 1.90 18.5 88 1.45
## 103 2 12.34 2.45 2.46 21.0 98 2.56
## 104 2 11.82 1.72 1.88 19.5 86 2.50
## 105 2 12.51 1.73 1.98 20.5 85 2.20
## 106 2 12.42 2.55 2.27 22.0 90 1.68
## 107 2 12.25 1.73 2.12 19.0 80 1.65
## 108 2 12.72 1.75 2.28 22.5 84 1.38
## 109 2 12.22 1.29 1.94 19.0 92 2.36
## 110 2 11.61 1.35 2.70 20.0 94 2.74
## 111 2 11.46 3.74 1.82 19.5 107 3.18
## 112 2 12.52 2.43 2.17 21.0 88 2.55
## 113 2 11.76 2.68 2.92 20.0 103 1.75
## 114 2 11.41 0.74 2.50 21.0 88 2.48
## 115 2 12.08 1.39 2.50 22.5 84 2.56
## 116 2 11.03 1.51 2.20 21.5 85 2.46
## 117 2 11.82 1.47 1.99 20.8 86 1.98
## 118 2 12.42 1.61 2.19 22.5 108 2.00
## 119 2 12.77 3.43 1.98 16.0 80 1.63
## 120 2 12.00 3.43 2.00 19.0 87 2.00
## 121 2 11.45 2.40 2.42 20.0 96 2.90
## 122 2 11.56 2.05 3.23 28.5 119 3.18
## 123 2 12.42 4.43 2.73 26.5 102 2.20
## 124 2 13.05 5.80 2.13 21.5 86 2.62
## 125 2 11.87 4.31 2.39 21.0 82 2.86
## 126 2 12.07 2.16 2.17 21.0 85 2.60
## 127 2 12.43 1.53 2.29 21.5 86 2.74
## 128 2 11.79 2.13 2.78 28.5 92 2.13
## 129 2 12.37 1.63 2.30 24.5 88 2.22
## 130 2 12.04 4.30 2.38 22.0 80 2.10
## 131 3 12.86 1.35 2.32 18.0 122 1.51
## 132 3 12.88 2.99 2.40 20.0 104 1.30
## 133 3 12.81 2.31 2.40 24.0 98 1.15
## 134 3 12.70 3.55 2.36 21.5 106 1.70
## 135 3 12.51 1.24 2.25 17.5 85 2.00
## 136 3 12.60 2.46 2.20 18.5 94 1.62
## 137 3 12.25 4.72 2.54 21.0 89 1.38
## 138 3 12.53 5.51 2.64 25.0 96 1.79
## 139 3 13.49 3.59 2.19 19.5 88 1.62
## 140 3 12.84 2.96 2.61 24.0 101 2.32
## 141 3 12.93 2.81 2.70 21.0 96 1.54
## 142 3 13.36 2.56 2.35 20.0 89 1.40
## 143 3 13.52 3.17 2.72 23.5 97 1.55
## 144 3 13.62 4.95 2.35 20.0 92 2.00
## 145 3 12.25 3.88 2.20 18.5 112 1.38
## 146 3 13.16 3.57 2.15 21.0 102 1.50
## 147 3 13.88 5.04 2.23 20.0 80 0.98
## 148 3 12.87 4.61 2.48 21.5 86 1.70
## 149 3 13.32 3.24 2.38 21.5 92 1.93
## 150 3 13.08 3.90 2.36 21.5 113 1.41
## 151 3 13.50 3.12 2.62 24.0 123 1.40
## 152 3 12.79 2.67 2.48 22.0 112 1.48
## 153 3 13.11 1.90 2.75 25.5 116 2.20
## 154 3 13.23 3.30 2.28 18.5 98 1.80
## 155 3 12.58 1.29 2.10 20.0 103 1.48
## 156 3 13.17 5.19 2.32 22.0 93 1.74
## 157 3 13.84 4.12 2.38 19.5 89 1.80
## 158 3 12.45 3.03 2.64 27.0 97 1.90
## 159 3 14.34 1.68 2.70 25.0 98 2.80
## 160 3 13.48 1.67 2.64 22.5 89 2.60
## 161 3 12.36 3.83 2.38 21.0 88 2.30
## 162 3 13.69 3.26 2.54 20.0 107 1.83
## 163 3 12.85 3.27 2.58 22.0 106 1.65
## 164 3 12.96 3.45 2.35 18.5 106 1.39
## 165 3 13.78 2.76 2.30 22.0 90 1.35
## 166 3 13.73 4.36 2.26 22.5 88 1.28
## 167 3 13.45 3.70 2.60 23.0 111 1.70
## 168 3 12.82 3.37 2.30 19.5 88 1.48
## 169 3 13.58 2.58 2.69 24.5 105 1.55
## 170 3 13.40 4.60 2.86 25.0 112 1.98
## 171 3 12.20 3.03 2.32 19.0 96 1.25
## 172 3 12.77 2.39 2.28 19.5 86 1.39
## 173 3 14.16 2.51 2.48 20.0 91 1.68
## 174 3 13.71 5.65 2.45 20.5 95 1.68
## 175 3 13.40 3.91 2.48 23.0 102 1.80
## 176 3 13.27 4.28 2.26 20.0 120 1.59
## 177 3 13.17 2.59 2.37 20.0 120 1.65
## 178 3 14.13 4.10 2.74 24.5 96 2.05
## Flavanoids Nonflavanoid_phenols Proanthocyanins Color_intensity Hue
## 1 3.06 0.28 2.29 5.640000 1.040
## 2 2.76 0.26 1.28 4.380000 1.050
## 3 3.24 0.30 2.81 5.680000 1.030
## 4 3.49 0.24 2.18 7.800000 0.860
## 5 2.69 0.39 1.82 4.320000 1.040
## 6 3.39 0.34 1.97 6.750000 1.050
## 7 2.52 0.30 1.98 5.250000 1.020
## 8 2.51 0.31 1.25 5.050000 1.060
## 9 2.98 0.29 1.98 5.200000 1.080
## 10 3.15 0.22 1.85 7.220000 1.010
## 11 3.32 0.22 2.38 5.750000 1.250
## 12 2.43 0.26 1.57 5.000000 1.170
## 13 2.76 0.29 1.81 5.600000 1.150
## 14 3.69 0.43 2.81 5.400000 1.250
## 15 3.64 0.29 2.96 7.500000 1.200
## 16 2.91 0.30 1.46 7.300000 1.280
## 17 3.14 0.33 1.97 6.200000 1.070
## 18 3.40 0.40 1.72 6.600000 1.130
## 19 3.93 0.32 1.86 8.700000 1.230
## 20 3.03 0.17 1.66 5.100000 0.960
## 21 3.17 0.24 2.10 5.650000 1.090
## 22 2.41 0.25 1.98 4.500000 1.030
## 23 2.88 0.27 1.69 3.800000 1.110
## 24 2.37 0.26 1.46 3.930000 1.090
## 25 2.61 0.28 1.66 3.520000 1.120
## 26 2.68 0.47 1.92 3.580000 1.130
## 27 2.94 0.34 1.45 4.800000 0.920
## 28 2.19 0.27 1.35 3.950000 1.020
## 29 2.97 0.37 1.76 4.500000 1.250
## 30 2.33 0.26 1.98 4.700000 1.040
## 31 3.25 0.29 2.38 5.700000 1.190
## 32 3.19 0.22 1.95 6.900000 1.090
## 33 2.69 0.42 1.97 3.840000 1.230
## 34 2.74 0.50 1.35 5.400000 1.250
## 35 2.53 0.29 1.54 4.200000 1.100
## 36 2.98 0.26 1.86 5.100000 1.040
## 37 2.68 0.34 1.36 4.600000 1.090
## 38 2.43 0.29 1.44 4.250000 1.120
## 39 2.64 0.28 1.37 3.700000 1.180
## 40 3.04 0.20 2.08 5.100000 0.890
## 41 3.29 0.34 2.34 6.130000 0.950
## 42 2.68 0.27 1.48 4.280000 0.910
## 43 3.56 0.17 1.70 5.430000 0.880
## 44 2.63 0.32 1.66 4.360000 0.820
## 45 3.00 0.28 2.03 5.040000 0.880
## 46 2.65 0.30 1.25 5.240000 0.870
## 47 3.17 0.27 2.19 4.900000 1.040
## 48 3.39 0.21 2.14 6.100000 0.910
## 49 2.92 0.32 2.38 6.200000 1.070
## 50 3.54 0.32 2.08 8.900000 1.120
## 51 3.27 0.17 2.91 7.200000 1.120
## 52 2.99 0.22 2.29 5.600000 1.240
## 53 3.74 0.32 1.87 7.050000 1.010
## 54 2.79 0.39 1.68 6.300000 1.130
## 55 2.90 0.21 1.62 5.850000 0.920
## 56 2.78 0.20 2.45 6.250000 0.980
## 57 3.00 0.26 2.03 6.380000 0.940
## 58 3.23 0.31 1.66 6.000000 1.070
## 59 3.67 0.19 2.04 6.800000 0.890
## 60 0.57 0.28 0.42 1.950000 1.050
## 61 1.09 0.63 0.41 3.270000 1.250
## 62 1.41 0.53 0.62 5.750000 0.980
## 63 1.79 0.32 0.73 3.800000 1.230
## 64 3.10 0.19 1.87 4.450000 1.220
## 65 1.75 0.45 1.03 2.950000 1.450
## 66 2.65 0.37 2.08 4.600000 1.190
## 67 3.18 0.26 2.28 5.300000 1.120
## 68 2.00 0.27 1.04 4.680000 1.120
## 69 1.30 0.55 0.42 3.170000 1.020
## 70 1.28 0.14 2.50 2.850000 1.280
## 71 1.02 0.37 1.46 3.050000 0.906
## 72 2.86 0.21 1.87 3.380000 1.360
## 73 1.84 0.27 1.03 3.740000 0.980
## 74 2.89 0.21 1.96 3.350000 1.310
## 75 2.14 0.13 1.65 3.210000 0.990
## 76 1.57 0.34 1.15 3.800000 1.230
## 77 2.03 0.24 1.46 4.600000 1.190
## 78 1.32 0.43 0.95 2.650000 0.960
## 79 1.85 0.35 2.76 3.400000 1.060
## 80 2.55 0.43 1.95 2.570000 1.190
## 81 2.26 0.30 1.43 2.500000 1.380
## 82 2.53 0.26 1.77 3.900000 1.160
## 83 1.58 0.40 1.40 2.200000 1.310
## 84 1.59 0.61 1.62 4.800000 0.840
## 85 2.21 0.22 2.35 3.050000 0.790
## 86 1.94 0.30 1.46 2.620000 1.230
## 87 1.69 0.43 1.56 2.450000 1.330
## 88 1.61 0.40 1.34 2.600000 1.360
## 89 1.69 0.48 1.35 2.800000 1.000
## 90 1.59 0.42 1.38 1.740000 1.070
## 91 1.50 0.52 1.64 2.400000 1.080
## 92 1.25 0.50 1.63 3.600000 1.050
## 93 1.46 0.58 1.62 3.050000 0.960
## 94 2.25 0.25 1.99 2.150000 1.150
## 95 2.26 0.17 1.35 3.250000 1.160
## 96 2.27 0.32 3.28 2.600000 1.160
## 97 0.99 0.14 1.56 2.500000 0.950
## 98 2.50 0.29 1.77 2.900000 1.230
## 99 3.75 0.24 1.95 4.500000 1.040
## 100 2.99 0.45 2.81 2.300000 1.420
## 101 2.17 0.26 1.40 3.300000 1.270
## 102 1.36 0.29 1.35 2.450000 1.040
## 103 2.11 0.34 1.31 2.800000 0.800
## 104 1.64 0.37 1.42 2.060000 0.940
## 105 1.92 0.32 1.48 2.940000 1.040
## 106 1.84 0.66 1.42 2.700000 0.860
## 107 2.03 0.37 1.63 3.400000 1.000
## 108 1.76 0.48 1.63 3.300000 0.880
## 109 2.04 0.39 2.08 2.700000 0.860
## 110 2.92 0.29 2.49 2.650000 0.960
## 111 2.58 0.24 3.58 2.900000 0.750
## 112 2.27 0.26 1.22 2.000000 0.900
## 113 2.03 0.60 1.05 3.800000 1.230
## 114 2.01 0.42 1.44 3.080000 1.100
## 115 2.29 0.43 1.04 2.900000 0.930
## 116 2.17 0.52 2.01 1.900000 1.710
## 117 1.60 0.30 1.53 1.950000 0.950
## 118 2.09 0.34 1.61 2.060000 1.060
## 119 1.25 0.43 0.83 3.400000 0.700
## 120 1.64 0.37 1.87 1.280000 0.930
## 121 2.79 0.32 1.83 3.250000 0.800
## 122 5.08 0.47 1.87 6.000000 0.930
## 123 2.13 0.43 1.71 2.080000 0.920
## 124 2.65 0.30 2.01 2.600000 0.730
## 125 3.03 0.21 2.91 2.800000 0.750
## 126 2.65 0.37 1.35 2.760000 0.860
## 127 3.15 0.39 1.77 3.940000 0.690
## 128 2.24 0.58 1.76 3.000000 0.970
## 129 2.45 0.40 1.90 2.120000 0.890
## 130 1.75 0.42 1.35 2.600000 0.790
## 131 1.25 0.21 0.94 4.100000 0.760
## 132 1.22 0.24 0.83 5.400000 0.740
## 133 1.09 0.27 0.83 5.700000 0.660
## 134 1.20 0.17 0.84 5.000000 0.780
## 135 0.58 0.60 1.25 5.450000 0.750
## 136 0.66 0.63 0.94 7.100000 0.730
## 137 0.47 0.53 0.80 3.850000 0.750
## 138 0.60 0.63 1.10 5.000000 0.820
## 139 0.48 0.58 0.88 5.700000 0.810
## 140 0.60 0.53 0.81 4.920000 0.890
## 141 0.50 0.53 0.75 4.600000 0.770
## 142 0.50 0.37 0.64 5.600000 0.700
## 143 0.52 0.50 0.55 4.350000 0.890
## 144 0.80 0.47 1.02 4.400000 0.910
## 145 0.78 0.29 1.14 8.210000 0.650
## 146 0.55 0.43 1.30 4.000000 0.600
## 147 0.34 0.40 0.68 4.900000 0.580
## 148 0.65 0.47 0.86 7.650000 0.540
## 149 0.76 0.45 1.25 8.420000 0.550
## 150 1.39 0.34 1.14 9.400000 0.570
## 151 1.57 0.22 1.25 8.600000 0.590
## 152 1.36 0.24 1.26 10.800000 0.480
## 153 1.28 0.26 1.56 7.100000 0.610
## 154 0.83 0.61 1.87 10.520000 0.560
## 155 0.58 0.53 1.40 7.600000 0.580
## 156 0.63 0.61 1.55 7.900000 0.600
## 157 0.83 0.48 1.56 9.010000 0.570
## 158 0.58 0.63 1.14 7.500000 0.670
## 159 1.31 0.53 2.70 13.000000 0.570
## 160 1.10 0.52 2.29 11.750000 0.570
## 161 0.92 0.50 1.04 7.650000 0.560
## 162 0.56 0.50 0.80 5.880000 0.960
## 163 0.60 0.60 0.96 5.580000 0.870
## 164 0.70 0.40 0.94 5.280000 0.680
## 165 0.68 0.41 1.03 9.580000 0.700
## 166 0.47 0.52 1.15 6.620000 0.780
## 167 0.92 0.43 1.46 10.680000 0.850
## 168 0.66 0.40 0.97 10.260000 0.720
## 169 0.84 0.39 1.54 8.660000 0.740
## 170 0.96 0.27 1.11 8.500000 0.670
## 171 0.49 0.40 0.73 5.500000 0.660
## 172 0.51 0.48 0.64 9.899999 0.570
## 173 0.70 0.44 1.24 9.700000 0.620
## 174 0.61 0.52 1.06 7.700000 0.640
## 175 0.75 0.43 1.41 7.300000 0.700
## 176 0.69 0.43 1.35 10.200000 0.590
## 177 0.68 0.53 1.46 9.300000 0.600
## 178 0.76 0.56 1.35 9.200000 0.610
## OD280_OD315 Proline cluster
## 1 3.92 1065 2
## 2 3.40 1050 2
## 3 3.17 1185 2
## 4 3.45 1480 2
## 5 2.93 735 2
## 6 2.85 1450 2
## 7 3.58 1290 2
## 8 3.58 1295 2
## 9 2.85 1045 2
## 10 3.55 1045 2
## 11 3.17 1510 2
## 12 2.82 1280 2
## 13 2.90 1320 2
## 14 2.73 1150 2
## 15 3.00 1547 2
## 16 2.88 1310 2
## 17 2.65 1280 2
## 18 2.57 1130 2
## 19 2.82 1680 2
## 20 3.36 845 2
## 21 3.71 780 2
## 22 3.52 770 2
## 23 4.00 1035 2
## 24 3.63 1015 2
## 25 3.82 845 2
## 26 3.20 830 2
## 27 3.22 1195 2
## 28 2.77 1285 2
## 29 3.40 915 2
## 30 3.59 1035 2
## 31 2.71 1285 2
## 32 2.88 1515 2
## 33 2.87 990 2
## 34 3.00 1235 2
## 35 2.87 1095 2
## 36 3.47 920 2
## 37 2.78 880 2
## 38 2.51 1105 2
## 39 2.69 1020 2
## 40 3.53 760 2
## 41 3.38 795 2
## 42 3.00 1035 2
## 43 3.56 1095 2
## 44 3.00 680 2
## 45 3.35 885 2
## 46 3.33 1080 2
## 47 3.44 1065 2
## 48 3.33 985 2
## 49 2.75 1060 2
## 50 3.10 1260 2
## 51 2.91 1150 2
## 52 3.37 1265 2
## 53 3.26 1190 2
## 54 2.93 1375 2
## 55 3.20 1060 2
## 56 3.03 1120 2
## 57 3.31 970 2
## 58 2.84 1270 2
## 59 2.87 1285 2
## 60 1.82 520 1
## 61 1.67 680 1
## 62 1.59 450 1
## 63 2.46 630 1
## 64 2.87 420 1
## 65 2.23 355 1
## 66 2.30 678 1
## 67 3.18 502 1
## 68 3.48 510 1
## 69 1.93 750 1
## 70 3.07 718 1
## 71 1.82 870 1
## 72 3.16 410 1
## 73 2.78 472 1
## 74 3.50 985 2
## 75 3.13 886 1
## 76 2.14 428 1
## 77 2.48 392 1
## 78 2.52 500 1
## 79 2.31 750 1
## 80 3.13 463 1
## 81 3.12 278 1
## 82 3.14 714 1
## 83 2.72 630 1
## 84 2.01 515 3
## 85 3.08 520 1
## 86 3.16 450 1
## 87 2.26 495 1
## 88 3.21 562 1
## 89 2.75 680 1
## 90 3.21 625 1
## 91 2.27 480 1
## 92 2.65 450 1
## 93 2.06 495 1
## 94 3.30 290 1
## 95 2.96 345 1
## 96 2.63 937 2
## 97 2.26 625 1
## 98 2.74 428 1
## 99 2.77 660 1
## 100 2.83 406 1
## 101 2.96 710 1
## 102 2.77 562 1
## 103 3.38 438 1
## 104 2.44 415 1
## 105 3.57 672 1
## 106 3.30 315 1
## 107 3.17 510 1
## 108 2.42 488 1
## 109 3.02 312 1
## 110 3.26 680 1
## 111 2.81 562 1
## 112 2.78 325 1
## 113 2.50 607 1
## 114 2.31 434 1
## 115 3.19 385 1
## 116 2.87 407 1
## 117 3.33 495 1
## 118 2.96 345 1
## 119 2.12 372 1
## 120 3.05 564 1
## 121 3.39 625 1
## 122 3.69 465 1
## 123 3.12 365 1
## 124 3.10 380 1
## 125 3.64 380 1
## 126 3.28 378 1
## 127 2.84 352 1
## 128 2.44 466 1
## 129 2.78 342 1
## 130 2.57 580 1
## 131 1.29 630 3
## 132 1.42 530 3
## 133 1.36 560 3
## 134 1.29 600 3
## 135 1.51 650 3
## 136 1.58 695 3
## 137 1.27 720 3
## 138 1.69 515 3
## 139 1.82 580 3
## 140 2.15 590 3
## 141 2.31 600 3
## 142 2.47 780 3
## 143 2.06 520 3
## 144 2.05 550 3
## 145 2.00 855 3
## 146 1.68 830 3
## 147 1.33 415 3
## 148 1.86 625 3
## 149 1.62 650 3
## 150 1.33 550 3
## 151 1.30 500 3
## 152 1.47 480 3
## 153 1.33 425 3
## 154 1.51 675 3
## 155 1.55 640 3
## 156 1.48 725 3
## 157 1.64 480 3
## 158 1.73 880 3
## 159 1.96 660 3
## 160 1.78 620 3
## 161 1.58 520 3
## 162 1.82 680 3
## 163 2.11 570 3
## 164 1.75 675 3
## 165 1.68 615 3
## 166 1.75 520 3
## 167 1.56 695 3
## 168 1.75 685 3
## 169 1.80 750 3
## 170 1.92 630 3
## 171 1.83 510 3
## 172 1.63 470 3
## 173 1.71 660 3
## 174 1.74 740 3
## 175 1.56 750 3
## 176 1.56 835 3
## 177 1.62 840 3
## 178 1.60 560 3Paso 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 Class Alcohol Malic_acid Ash Alcalinity_of_ash Magnesium
## 1 1 2.000000 12.25412 1.914265 2.239118 20.07941 93.04412
## 2 2 1.032787 13.71148 1.997049 2.453770 17.28197 107.78689
## 3 3 2.979592 13.15163 3.344490 2.434694 21.43878 99.02041
## Total_phenols Flavanoids Nonflavanoid_phenols Proanthocyanins Color_intensity
## 1 2.248971 2.0733824 0.3629412 1.601324 3.064706
## 2 2.842131 2.9691803 0.2891803 1.922951 5.444590
## 3 1.678163 0.7979592 0.4508163 1.163061 7.343265
## Hue OD280_OD315 Proline cluster
## 1 1.0542059 2.788529 506.5882 1
## 2 1.0677049 3.154754 1110.6393 2
## 3 0.6859184 1.690204 627.5510 3Conclusión
La segmentación o clusters es un algoritmo útil para identificar el cultivar correspondiente a cada vino.
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