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 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/ve3WBa

Paso 2. Obtener los datos

datos <- read.csv("C:\\Users\\mari0\\OneDrive\\Documents\\R Studio\\IA con impacto empresarial\\Modulo 2\\BD\\wine.csv")

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.   :1680.0

Paso 4. Escalar la base de datos

df <- scale(datos)

Paso 5. Cantidad de grupos

grupos <- 3

Paso 6. Generar los segmentos

segmentos <- kmeans(df,grupos)
segmentos
## K-means clustering with 3 clusters of sizes 62, 51, 65
## 
## Cluster means:
##      Alcohol Malic_Acid        Ash Ash_Alcanity   Magnesium Total_Phenols
## 1  0.8328826 -0.3029551  0.3636801   -0.6084749  0.57596208    0.88274724
## 2  0.1644436  0.8690954  0.1863726    0.5228924 -0.07526047   -0.97657548
## 3 -0.9234669 -0.3929331 -0.4931257    0.1701220 -0.49032869   -0.07576891
##    Flavanoids Nonflavanoid_Phenols Proanthocyanins Color_Intensity        Hue
## 1  0.97506900          -0.56050853      0.57865427       0.1705823  0.4726504
## 2 -1.21182921           0.72402116     -0.77751312       0.9388902 -1.1615122
## 3  0.02075402          -0.03343924      0.05810161      -0.8993770  0.4605046
##        OD280    Proline
## 1  0.7770551  1.1220202
## 2 -1.2887761 -0.4059428
## 3  0.2700025 -0.7517257
## 
## 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 1 1 1 1 1 1 1 1 1 3 3 2 3 3 3 3 3 3 3 3 3 3 3 1
##  [75] 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [112] 3 3 3 3 3 3 3 2 3 3 1 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [149] 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
## 
## Within cluster sum of squares by cluster:
## [1] 385.6983 326.3537 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

Paso 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 13.67677   1.997903 2.466290     17.46290 107.96774      2.847581
## 2       2 13.13412   3.307255 2.417647     21.24118  98.66667      1.683922
## 3       3 12.25092   1.897385 2.231231     20.06308  92.73846      2.247692
##   Flavanoids Nonflavanoid_Phenols Proanthocyanins Color_Intensity       Hue
## 1  3.0032258            0.2920968        1.922097        5.453548 1.0654839
## 2  0.8188235            0.4519608        1.145882        7.234706 0.6919608
## 3  2.0500000            0.3576923        1.624154        2.973077 1.0627077
##      OD280   Proline cluster
## 1 3.163387 1100.2258       1
## 2 1.696667  619.0588       2
## 3 2.803385  510.1692       3

Conclusión

La segmentación o clusters es un algoritmo útil para identificar el cultivar correspondiente a cada vino.file