Contexto

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 cultivares.

Paso 1. Instalar paquetes y llamar librerías 

#install.packages("cluster") # Análisis de Agrupamiento
library(cluster)
#install.packages("ggplot2") #Graficar
library(ggplot2)
#install.packages("data.table") #Manejo de muchos datos
library(data.table)
#install.packages("factoextra") #Gráfico optimización de número de clusters
library(factoextra)

Paso 2. Obtener los datos 

df1 <- read.csv("C:/Users/corsa/OneDrive - CORSA Transportes SA de CV/Escritorio/TEC/wine.csv")

Paso 3. Entender los datos 

summary(df1)
##     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.   :1680.0
str(df1)
## '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 4. Escalar los datos 

# Solo si los datos no están en la misma escala
datos_escalados <- scale(df1)

Paso 5. Determinar número de grupos 

# Siempre es un valor inicial "cualquiera", luego se optimiza.
plot(datos_escalados)

grupos1 <- 3

Paso 6. Generar los grupos 

set.seed(123)
clusters1 <- kmeans(datos_escalados,grupos1)
clusters1
## 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. Optimizar el número de grupos 

set.seed(123)
optimización <- clusGap(datos_escalados,FUN=kmeans,nstart=1,K.max=10)
#El K.max normalmente es 10, en este ejercicio al ser 8 datos se dejó en 7.
plot(optimización,xlab="Número de clusters k")

# Se selecciona como óptimo el primer númeor de clusters

Paso 8. Graficar los grupos 

fviz_cluster(clusters1,data=datos_escalados)

Paso 9. Agregar clusters a la base de datos 

df1clusters <- cbind(datos_escalados,cluster = clusters1$cluster)
head(df1clusters)
##        Alcohol  Malic_Acid        Ash Ash_Alcanity  Magnesium Total_Phenols
## [1,] 1.5143408 -0.56066822  0.2313998   -1.1663032 1.90852151     0.8067217
## [2,] 0.2455968 -0.49800856 -0.8256672   -2.4838405 0.01809398     0.5670481
## [3,] 0.1963252  0.02117152  1.1062139   -0.2679823 0.08810981     0.8067217
## [4,] 1.6867914 -0.34583508  0.4865539   -0.8069748 0.92829983     2.4844372
## [5,] 0.2948684  0.22705328  1.8352256    0.4506745 1.27837900     0.8067217
## [6,] 1.4773871 -0.51591132  0.3043010   -1.2860793 0.85828399     1.5576991
##      Flavanoids Nonflavanoid_Phenols Proanthocyanins Color_Intensity        Hue
## [1,]  1.0319081           -0.6577078       1.2214385       0.2510088  0.3611585
## [2,]  0.7315653           -0.8184106      -0.5431887      -0.2924962  0.4049085
## [3,]  1.2121137           -0.4970050       2.1299594       0.2682629  0.3174085
## [4,]  1.4623994           -0.9791134       1.0292513       1.1827317 -0.4263410
## [5,]  0.6614853            0.2261576       0.4002753      -0.3183774  0.3611585
## [6,]  1.3622851           -0.1755994       0.6623487       0.7298108  0.4049085
##          OD280     Proline cluster
## [1,] 1.8427215  1.01015939       2
## [2,] 1.1103172  0.96252635       2
## [3,] 0.7863692  1.39122370       2
## [4,] 1.1807407  2.32800680       2
## [5,] 0.4483365 -0.03776747       2
## [6,] 0.3356589  2.23274072       2

Conclusiones

La técnica de clustering permite identificar patrones o grupos naturales en los datos sin necesidad de etiquetas previas.En este caso si fue necesario utilizar la escala de datos para poder trabajar con ellos durante el analisis y el agrupamiento de los clusters.

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