library(cluster)
library(ggplot2)
library(data.table)
library(factoextra)
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
spotify <- data <- read.csv("C:\\Users\\eleyva1\\Downloads\\Most Streamed Spotify Songs 2024.csv")
df <- data.frame(x=c(2,2,8,5,7,6,1,4), y=c(10,5,4,8,5,4,2,9))
grupos <- 3 
segmentos <- kmeans(df,4)
segmentos
## K-means clustering with 4 clusters of sizes 1, 3, 3, 1
## 
## Cluster means:
##          x        y
## 1 1.000000 2.000000
## 2 7.000000 4.333333
## 3 3.666667 9.000000
## 4 2.000000 5.000000
## 
## Clustering vector:
## [1] 3 4 2 3 2 2 1 3
## 
## Within cluster sum of squares by cluster:
## [1] 0.000000 2.666667 6.666667 0.000000
##  (between_SS / total_SS =  90.7 %)
## 
## Available components:
## 
## [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
## [6] "betweenss"    "size"         "iter"         "ifault"
asignacion <- cbind(df, cluster = segmentos$cluster)
asignacion
##   x  y cluster
## 1 2 10       3
## 2 2  5       4
## 3 8  4       2
## 4 5  8       3
## 5 7  5       2
## 6 6  4       2
## 7 1  2       1
## 8 4  9       3
fviz_cluster(segmentos, data=df)

set.seed(123)
optimizacion <- clusGap(df, FUN=kmeans, nstart=1, K.max =7)
plot(optimizacion, xlab="Número de clusters k")

Conclusión

La segmentación o clusters es un algoritmo útil para las empresas que desean clasificar sus clientes y dirigir campañas de marketing más enfocadas y especializadas.
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