A23_MAE118_BA22008

Ejercicio 1

1- Explique el análisis de conglomerados

Su aplicación consiste en agrupar datos que poseen similitudes entre sí, un ejemplo podría ser un proyecto de investigación el cual es asignado a un grupo de estudiantes y como líder o catedrático de grupo quieres organizarlos en grupos con el análisis de conglomerados podrías hacerlo, pero para mayor desarrollo podríamos aplicar este método a una tienda para conocer las preferencias de los clientes y agruparlos en base a lo que llegan a demandar, esto te ayudará a entender los datos que pueden parecer desordenados pero al aplicar este análisis tendrás una mayor visibilidad de ellos.

Fuente: [daysimaria2017]

Ejercicio 2 y 3

Cuadro Comparativo del Análisis Clustrer

Análisis de clúster

Técnicas disponibles

Ventajas

Desventajas

Jerárquico

Une y a la vez divide en base a las características que poseen los métodos a aplicarse, eso si que si se agrega una unidad a un grupo esta no se puede deshacer porque solo puede ser asignada una sola vez.

<![if !supportLists]>· <![endif]>Aglomeración aglomerativa.

<![if !supportLists]>· <![endif]>Dendograma.

<![if !supportLists]>· <![endif]>Mapa de calor.

Explorar diversas estructuras jerárquicas, no requiere especificar el número de clúster, perfecto para conjunto de datos pequeños.

La interpretación del diagrama, las asignaciones de agrupamiento son secuenciales, errores que pueden propagarse y afectar la solución final

No jerárquico

Clasifican las observaciones en un conjunto de datos, estos se asignan de acuerdo a los parentescos o cosas en común que posean.

<![if !supportLists]>· <![endif]>Agrupamiento de las k-medias.

<![if !supportLists]>· <![endif]>Algoritmo PAM.

<![if !supportLists]>· <![endif]>CLARA.

Calcular k-medias para un rango de k valores, el ritmo de algoritmos de las k-means varias veces y el PAM que es un valor poco sensible a los atípicos.

Los resultados finales son sensibles a la selección de los aleatoria inicial de los datos, también a los valores atípicos y por otro lado si reorganizas los datos puedes obtener una solución diferente.

Fuentes: [Alboukadel2017] [daysimaria2017]

Ejercicio 4

Capítulo 4: K-means Cluster

data("USArrests") # Loading the data set
df <- scale(USArrests) # Scaling the data
# View the firt 3 rows of the data
head(df, n = 3)

##             Murder   Assault   UrbanPop         Rape
## Alabama 1.24256408 0.7828393 -0.5209066 -0.003416473
## Alaska  0.50786248 1.1068225 -1.2117642  2.484202941
## Arizona 0.07163341 1.4788032  0.9989801  1.042878388

4.3.3 Estimación del numero de clusters

library(factoextra)
fviz_nbclust(df, kmeans, method = "wss") +
geom_vline(xintercept = 4, linetype = 2)

Usaremos 4 clusters, k=4

4.3.4 Computamos K-mean

# Compute k-means with k = 4
set.seed(123)
km.res <- kmeans(df, 4, nstart = 25)
print(km.res)

## K-means clustering with 4 clusters of sizes 8, 13, 16, 13
## 
## Cluster means:
##       Murder    Assault   UrbanPop        Rape
## 1  1.4118898  0.8743346 -0.8145211  0.01927104
## 2 -0.9615407 -1.1066010 -0.9301069 -0.96676331
## 3 -0.4894375 -0.3826001  0.5758298 -0.26165379
## 4  0.6950701  1.0394414  0.7226370  1.27693964
## 
## Clustering vector:
##        Alabama         Alaska        Arizona       Arkansas     California 
##              1              4              4              1              4 
##       Colorado    Connecticut       Delaware        Florida        Georgia 
##              4              3              3              4              1 
##         Hawaii          Idaho       Illinois        Indiana           Iowa 
##              3              2              4              3              2 
##         Kansas       Kentucky      Louisiana          Maine       Maryland 
##              3              2              1              2              4 
##  Massachusetts       Michigan      Minnesota    Mississippi       Missouri 
##              3              4              2              1              4 
##        Montana       Nebraska         Nevada  New Hampshire     New Jersey 
##              2              2              4              2              3 
##     New Mexico       New York North Carolina   North Dakota           Ohio 
##              4              4              1              2              3 
##       Oklahoma         Oregon   Pennsylvania   Rhode Island South Carolina 
##              3              3              3              3              1 
##   South Dakota      Tennessee          Texas           Utah        Vermont 
##              2              1              4              3              2 
##       Virginia     Washington  West Virginia      Wisconsin        Wyoming 
##              3              3              2              2              3 
## 
## Within cluster sum of squares by cluster:
## [1]  8.316061 11.952463 16.212213 19.922437
##  (between_SS / total_SS =  71.2 %)
## 
## Available components:
## 
## [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
## [6] "betweenss"    "size"         "iter"         "ifault"

De que querer comparar las medias de cada variable por el cluster

aggregate(USArrests, by=list(cluster=km.res$cluster), mean)

Pero de querer añadir un punto de clasificacion

dd <- cbind(USArrests, cluster = km.res$cluster)
head(dd)

4.3.5 Accedemos a los resultado de la funcion de k-mean()

km.res$cluster ##Sacamos el numero de cluster para cada observacion

##        Alabama         Alaska        Arizona       Arkansas     California 
##              1              4              4              1              4 
##       Colorado    Connecticut       Delaware        Florida        Georgia 
##              4              3              3              4              1 
##         Hawaii          Idaho       Illinois        Indiana           Iowa 
##              3              2              4              3              2 
##         Kansas       Kentucky      Louisiana          Maine       Maryland 
##              3              2              1              2              4 
##  Massachusetts       Michigan      Minnesota    Mississippi       Missouri 
##              3              4              2              1              4 
##        Montana       Nebraska         Nevada  New Hampshire     New Jersey 
##              2              2              4              2              3 
##     New Mexico       New York North Carolina   North Dakota           Ohio 
##              4              4              1              2              3 
##       Oklahoma         Oregon   Pennsylvania   Rhode Island South Carolina 
##              3              3              3              3              1 
##   South Dakota      Tennessee          Texas           Utah        Vermont 
##              2              1              4              3              2 
##       Virginia     Washington  West Virginia      Wisconsin        Wyoming 
##              3              3              2              2              3

head(km.res$cluster, 4) # Las primeras 4

##  Alabama   Alaska  Arizona Arkansas 
##        1        4        4        1

# Pedimos el tamaño
km.res$size

## [1]  8 13 16 13

# Y ahora pedimos la media
km.res$centers

##       Murder    Assault   UrbanPop        Rape
## 1  1.4118898  0.8743346 -0.8145211  0.01927104
## 2 -0.9615407 -1.1066010 -0.9301069 -0.96676331
## 3 -0.4894375 -0.3826001  0.5758298 -0.26165379
## 4  0.6950701  1.0394414  0.7226370  1.27693964

4.3.6 Ahora para visualizar el cluster k-mean

fviz_cluster(km.res, data = df,
palette = c("#2E9FDF", "#00AFBB", "#E7B800", "#FC4E07"),
ellipse.type = "euclid", # Concentration ellipse
star.plot = TRUE, # Add segments from centroids to items
repel = TRUE, # Avoid label overplotting (slow)
ggtheme = theme_minimal()
)

Cápitulo 5: K-Medoids

# Datos
data("USArrests") # Load the data set
df <- scale(USArrests) # Scale the data
head(df, n = 3) # View the firt 3 rows of the data

##             Murder   Assault   UrbanPop         Rape
## Alabama 1.24256408 0.7828393 -0.5209066 -0.003416473
## Alaska  0.50786248 1.1068225 -1.2117642  2.484202941
## Arizona 0.07163341 1.4788032  0.9989801  1.042878388

5.3.3 Estimamos el numero optimo de clusters

library(cluster)
library(factoextra)
fviz_nbclust(df, pam, method = "silhouette")+
theme_classic()

5.3.4 Computamos el PAM clustering

pam.res <- pam(df, 2)
print(pam.res)

## Medoids:
##            ID     Murder    Assault   UrbanPop       Rape
## New Mexico 31  0.8292944  1.3708088  0.3081225  1.1603196
## Nebraska   27 -0.8008247 -0.8250772 -0.2445636 -0.5052109
## Clustering vector:
##        Alabama         Alaska        Arizona       Arkansas     California 
##              1              1              1              2              1 
##       Colorado    Connecticut       Delaware        Florida        Georgia 
##              1              2              2              1              1 
##         Hawaii          Idaho       Illinois        Indiana           Iowa 
##              2              2              1              2              2 
##         Kansas       Kentucky      Louisiana          Maine       Maryland 
##              2              2              1              2              1 
##  Massachusetts       Michigan      Minnesota    Mississippi       Missouri 
##              2              1              2              1              1 
##        Montana       Nebraska         Nevada  New Hampshire     New Jersey 
##              2              2              1              2              2 
##     New Mexico       New York North Carolina   North Dakota           Ohio 
##              1              1              1              2              2 
##       Oklahoma         Oregon   Pennsylvania   Rhode Island South Carolina 
##              2              2              2              2              1 
##   South Dakota      Tennessee          Texas           Utah        Vermont 
##              2              1              1              2              2 
##       Virginia     Washington  West Virginia      Wisconsin        Wyoming 
##              2              2              2              2              2 
## Objective function:
##    build     swap 
## 1.441358 1.368969 
## 
## Available components:
##  [1] "medoids"    "id.med"     "clustering" "objective"  "isolation" 
##  [6] "clusinfo"   "silinfo"    "diss"       "call"       "data"

Para añadir un punto de clasificación a los datos originales

dd <- cbind(USArrests, cluster = pam.res$cluster)
head(dd, n = 3)

5.3.5 El acceso a los resultados de la funcion pam()

# Cluster medoids: New Mexico, Nebraska
pam.res$medoids

##                Murder    Assault   UrbanPop       Rape
## New Mexico  0.8292944  1.3708088  0.3081225  1.1603196
## Nebraska   -0.8008247 -0.8250772 -0.2445636 -0.5052109

# Cluster numbers
head(pam.res$clustering)

##    Alabama     Alaska    Arizona   Arkansas California   Colorado 
##          1          1          1          2          1          1

5.3.6 Visualizamos el cluster PAM

fviz_cluster(pam.res,
palette = c("#00AFBB", "#FC4E07"), # color palette
ellipse.type = "t", # Concentration ellipse
repel = TRUE, # Avoid label overplotting (slow)
ggtheme = theme_classic()
)

Cápitulo 6: CLARA- Conglomerando Grandes Aplicaciones

# El formato de los datos y su preparacion
set.seed(1234)
# Generate 500 objects, divided into 2 clusters.
df <- rbind(cbind(rnorm(200,0,8), rnorm(200,0,8)),
cbind(rnorm(300,50,8), rnorm(300,50,8)))
# Specify column and row names
colnames(df) <- c("x", "y")

rownames(df) <- paste0("S", 1:nrow(df))
# Previewing the data
head(df, nrow = 6)

##             x        y
## S1  -9.656526 3.881815
## S2   2.219434 5.574150
## S3   8.675529 1.484111
## S4 -18.765582 5.605868
## S5   3.432998 2.493448
## S6   4.048447 6.083699

6.3.3 Estimar el número de clusters optimos

library(cluster)
library(factoextra)
fviz_nbclust(df, clara, method = "silhouette")+
theme_classic()

6.3.4 Computamos CLARA

# Compute CLARA
clara.res <- clara(df, 2, samples = 50, pamLike = TRUE)
# Print components of clara.res
print(clara.res)

## Call:     clara(x = df, k = 2, samples = 50, pamLike = TRUE) 
## Medoids:
##              x         y
## S121 -1.531137  1.145057
## S455 48.357304 50.233499
## Objective function:   9.87862
## Clustering vector:    Named int [1:500] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ...
##  - attr(*, "names")= chr [1:500] "S1" "S2" "S3" "S4" "S5" "S6" "S7" ...
## Cluster sizes:            200 300 
## Best sample:
##  [1] S37  S49  S54  S63  S68  S71  S76  S80  S82  S101 S103 S108 S109 S118 S121
## [16] S128 S132 S138 S144 S162 S203 S210 S216 S231 S234 S249 S260 S261 S286 S299
## [31] S304 S305 S312 S315 S322 S350 S403 S450 S454 S455 S456 S465 S488 S497
## 
## Available components:
##  [1] "sample"     "medoids"    "i.med"      "clustering" "objective" 
##  [6] "clusinfo"   "diss"       "call"       "silinfo"    "data"

Para añadir un punto de clasificacion en la data original

dd <- cbind(df, cluster = clara.res$cluster)
head(dd, n = 4)

##             x        y cluster
## S1  -9.656526 3.881815       1
## S2   2.219434 5.574150       1
## S3   8.675529 1.484111       1
## S4 -18.765582 5.605868       1

Para acceder a los resultados regresados por clara()

# Medoids
clara.res$medoids

##              x         y
## S121 -1.531137  1.145057
## S455 48.357304 50.233499

# Clustering
head(clara.res$clustering, 10)

##  S1  S2  S3  S4  S5  S6  S7  S8  S9 S10 
##   1   1   1   1   1   1   1   1   1   1

6.3.5 Visualizar el Cluster CLARA

fviz_cluster(clara.res,
palette = c("#00AFBB", "#FC4E07"), # color palette
ellipse.type = "t", # Concentration ellipse
geom = "point", pointsize = 1,
ggtheme = theme_classic()
)

Cápitulo 7: Conglomerados Aglomerativos

# Estructura de Datos y preparacion
# Load the data
data("USArrests")
# Standardize the data
df <- scale(USArrests)
# Show the first 6 rows
head(df, nrow = 6)

##                Murder   Assault   UrbanPop         Rape
## Alabama    1.24256408 0.7828393 -0.5209066 -0.003416473
## Alaska     0.50786248 1.1068225 -1.2117642  2.484202941
## Arizona    0.07163341 1.4788032  0.9989801  1.042878388
## Arkansas   0.23234938 0.2308680 -1.0735927 -0.184916602
## California 0.27826823 1.2628144  1.7589234  2.067820292
## Colorado   0.02571456 0.3988593  0.8608085  1.864967207

7.2.2 Similitud de medidas

# Compute the dissimilarity matrix
# df = the standardized data
res.dist <- dist(df, method = "euclidean")

Para que enseñe las primeras 6 columnas y filas

as.matrix(res.dist)[1:6, 1:6]

##             Alabama   Alaska  Arizona Arkansas California Colorado
## Alabama    0.000000 2.703754 2.293520 1.289810   3.263110 2.651067
## Alaska     2.703754 0.000000 2.700643 2.826039   3.012541 2.326519
## Arizona    2.293520 2.700643 0.000000 2.717758   1.310484 1.365031
## Arkansas   1.289810 2.826039 2.717758 0.000000   3.763641 2.831051
## California 3.263110 3.012541 1.310484 3.763641   0.000000 1.287619
## Colorado   2.651067 2.326519 1.365031 2.831051   1.287619 0.000000

7.2.3 Enlazamiento

res.hc <- hclust(d = res.dist, method = "ward.D2")

7.2.4 Dendogramas

# cex: label size
library("factoextra")
fviz_dend(res.hc, cex = 0.5)

7.2.5 Verificamos el árbol Cluster

# Compute cophentic distance
res.coph <- cophenetic(res.hc)
# Correlation between cophenetic distance and
# the original distance
cor(res.dist, res.coph)

## [1] 0.6975266

res.hc2 <- hclust(res.dist, method = "average")
cor(res.dist, cophenetic(res.hc2))

## [1] 0.7180382

7.4 Cortamos el dendograma en diferentes partes

# Cut tree into 4 groups
grp <- cutree(res.hc, k = 4)
head(grp, n = 4)

##  Alabama   Alaska  Arizona Arkansas 
##        1        2        2        3

# Number of members in each cluster
table(grp)

## grp
##  1  2  3  4 
##  7 12 19 12

# Get the names for the members of cluster 1
rownames(df)[grp == 1]

## [1] "Alabama"        "Georgia"        "Louisiana"      "Mississippi"   
## [5] "North Carolina" "South Carolina" "Tennessee"

# Cut in 4 groups and color by groups
fviz_dend(res.hc, k = 4, # Cut in four groups
cex = 0.5, # label size
k_colors = c("#2E9FDF", "#00AFBB", "#E7B800", "#FC4E07"),
color_labels_by_k = TRUE, # color labels by groups
rect = TRUE # Add rectangle around groups
)

Pero de preferirlo con el diagrama de dispersion usamos la función fviz_cluster()

fviz_cluster(list(data = df, cluster = grp),
palette = c("#2E9FDF", "#00AFBB", "#E7B800", "#FC4E07"),
ellipse.type = "convex", # Concentration ellipse
repel = TRUE, # Avoid label overplotting (slow)
show.clust.cent = FALSE, ggtheme = theme_minimal())

Capitulo 8: Comparación de Dendogramas

# Preparación de los datos
df <- scale(USArrests)
# Subset containing 10 rows
set.seed(123)
ss <- sample(1:50, 10)
df <- df[ss,]

8.2 Comparando dendogramas

library(dendextend)
# Compute distance matrix
res.dist <- dist(df, method = "euclidean")
# Compute 2 hierarchical clusterings
hc1 <- hclust(res.dist, method = "average")
hc2 <- hclust(res.dist, method = "ward.D2")
# Create two dendrograms
dend1 <- as.dendrogram (hc1)
dend2 <- as.dendrogram (hc2)
# Create a list to hold dendrograms
dend_list <- dendlist(dend1, dend2)

8.2.1 Comparación visual de los 2 dendogramas

tanglegram(dend1, dend2)

Se puede costumizar el tanglegrama

tanglegram(dend1, dend2,
highlight_distinct_edges = FALSE, # Turn-off dashed lines
common_subtrees_color_lines = FALSE, # Turn-off line colors
common_subtrees_color_branches = TRUE, # Color common branches
main = paste("entanglement =", round(entanglement(dend_list), 2))
)

8.2.2 La correlacion matriz entre una lista de dendogramas

Hay 2 métodos para la matriz de correlación entre las listas de arboles. El método Cophenetic y el metodo Baker

# Cophenetic correlation matrix
cor.dendlist(dend_list, method = "cophenetic")

##           [,1]      [,2]
## [1,] 1.0000000 0.9925544
## [2,] 0.9925544 1.0000000

# Baker correlation matrix
cor.dendlist(dend_list, method = "baker")

##           [,1]      [,2]
## [1,] 1.0000000 0.9895528
## [2,] 0.9895528 1.0000000

La Correlacion entre ambos se puede hacer de 2 maneras

# Cophenetic correlation coefficient
cor_cophenetic(dend1, dend2)

## [1] 0.9925544

# Baker correlation coefficient
cor_bakers_gamma(dend1, dend2)

## [1] 0.9895528

Es posible la comparación de multiples dendogramas y tambien es mas sencillo para simplificar el codigo

# Create multiple dendrograms by chaining
dend1 <- df %>% dist %>% hclust("complete") %>% as.dendrogram
dend2 <- df %>% dist %>% hclust("single") %>% as.dendrogram
dend3 <- df %>% dist %>% hclust("average") %>% as.dendrogram
dend4 <- df %>% dist %>% hclust("centroid") %>% as.dendrogram
# Compute correlation matrix
dend_list <- dendlist("Complete" = dend1, "Single" = dend2,
"Average" = dend3, "Centroid" = dend4)
cors <- cor.dendlist(dend_list)
# Print correlation matrix
round(cors, 2)

##          Complete Single Average Centroid
## Complete     1.00   0.46    0.45     0.30
## Single       0.46   1.00    0.23     0.17
## Average      0.45   0.23    1.00     0.31
## Centroid     0.30   0.17    0.31     1.00

Para visualizar la matriz de correlacion usando el paquete corrplot

# Visualize the correlation matrix using corrplot package
library(corrplot)
corrplot(cors, "pie", "lower")

Capítulo 9: Visualizando Dendogramas

# Load data
data(USArrests)
# Compute distances and hierarchical clustering
dd <- dist(scale(USArrests), method = "euclidean")
hc <- hclust(dd, method = "ward.D2")

9.1 Visualizando Dendogramas

Para crear un dendograma basico

library(factoextra)
fviz_dend(hc, cex = 0.5)

Se pueden usar los argumento de main, xlab, sub, y ylab para cambiar los nombres de los ejes

fviz_dend(hc, cex = 0.5,
main = "Dendrogram - ward.D2",
xlab = "Eje x", ylab = "Eje y", sub = "")

Para dibujar el dendograma en horizontal

fviz_dend(hc, cex = 0.5, horiz = TRUE)

De querer otro tipo de diseño también se puede usar el siguiente ejemplo

fviz_dend(hc, k = 4, # Cut in four groups
cex = 0.5, # label size
k_colors = c("#2E9FDF", "#00AFBB", "#E7B800", "#FC4E07"),
color_labels_by_k = TRUE, # color labels by groups
ggtheme = theme_gray() # Change theme
)

###De querer otros colores, se puede usar los de “journal of clinical oncology” o “jco”###

fviz_dend(hc, cex = 0.5, k = 4, # Cut in four groups
k_colors = "jco")

Para dibujarlo en horizontal

fviz_dend(hc, k = 4, cex = 0.4, horiz = TRUE, k_colors = "jco",
rect = TRUE, rect_border = "jco", rect_fill = TRUE)

También lo podemos dibujar como un dendograma circular

fviz_dend(hc, cex = 0.5, k = 4,
k_colors = "jco", type = "circular")

Otro diseño es el estilo de árbol filogenetico

require("igraph")
fviz_dend(hc, k = 4, k_colors = "jco",
type = "phylogenic", repel = TRUE)

Para usar otro método y hacer arboles filigenticos se puede usar la libreria “igraoh”

require("igraph")
fviz_dend(hc, k = 4, # Cut in four groups
k_colors = "jco",
type = "phylogenic", repel = TRUE,
phylo_layout = "layout.gem")

9.2 En caso de dendogramas en gran escala

Podemos hacer zoom a los dendogramas

fviz_dend(hc, xlim = c(1, 20), ylim = c(1, 8))

9.2.2 Trazar un sub-árbol de Dendogramas

# Create a plot of the whole dendrogram,
# and extract the dendrogram data
dend_plot <- fviz_dend(hc, k = 4, # Cut in four groups
cex = 0.5, # label size
k_colors = "jco"
)
dend_data <- attr(dend_plot, "dendrogram") # Extract dendrogram data
# Cut the dendrogram at height h = 10
dend_cuts <- cut(dend_data, h = 10)
# Visualize the truncated version containing
# two branches
fviz_dend(dend_cuts$upper)

# Plot the whole dendrogram
print(dend_plot)

# Plot subtree 1
fviz_dend(dend_cuts$lower[[1]], main = "Subtree 1")

# Plot subtree 2
fviz_dend(dend_cuts$lower[[2]], main = "Subtree 2")

También podemos trazar arboles circulares como el siguiente:

fviz_dend(dend_cuts$lower[[2]], type = "circular")

9.2.3 Para guardar el dendograma en una pagina grande de PDF

pdf("dendrogram.pdf", width=30, height=15) # Open a PDF
p <- fviz_dend(hc, k = 4, cex = 1, k_colors = "jco" ) # Do plotting
print(p)
dev.off() # Close the PDF

## png 
##   2

9.3 Manipulando dendogramas usando “dendextend”

Código Estándar de R para crear un dendograma

data <- scale(USArrests)
dist.res <- dist(data)
hc <- hclust(dist.res, method = "ward.D2")
dend <- as.dendrogram(hc)
plot(dend)

Código de R para crear un dendograma usando operadores de cadena

library(dendextend)
dend <- USArrests[1:5,] %>% # data
scale %>% # Scale the data
dist %>% # calculate a distance matrix,
hclust(method = "ward.D2") %>% # Hierarchical clustering
as.dendrogram # Turn the object into a dendrogram.
plot(dend)

Bibliografías

---
title: "A23_MAE118_BA22008"
author: "
-Favio Andres Bonilla Amaya
-Dina Esmeralda Umanzor Bonilla
-Fernanda Elizabeth Rodas Velasquez
"
date: "2024-11-23"
output: 
  rmdformats::readthedown:   # https://github.com/juba/rmdformats
    self_contained: true
    thumbnails: true
    lightbox: true
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csl: "apa.csl"  # Ruta al archivo CSL de estilo APA
bibliography: "bibliografia.bib"  # Ruta al archivo BibTeX de referencias
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(eval=TRUE, echo=TRUE, message=FALSE, warning=FALSE)
```
# Ejercicio 1
## *1- Explique el análisis de conglomerados* ##



   Su aplicación consiste en agrupar datos que poseen similitudes entre sí, un ejemplo podría ser un proyecto de investigación el cual es asignado a un grupo de estudiantes y como líder o catedrático de grupo quieres organizarlos en grupos con el análisis de conglomerados podrías hacerlo, pero para mayor desarrollo podríamos aplicar este método a una tienda para conocer las preferencias de los clientes y agruparlos en base a lo que llegan a demandar, esto te ayudará a entender los datos que pueden parecer desordenados  pero al aplicar este análisis tendrás una mayor visibilidad de ellos.

Fuente: [daysimaria2017]




# Ejercicio 2 y 3

## Cuadro Comparativo del Análisis Clustrer ##
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 </o:shapelayout></xml><![endif]-->
</head>

<body lang=ES-SV style='tab-interval:35.4pt;word-wrap:break-word'>

<div class=WordSection1>

<p class=MsoListParagraph><span lang=es-419 style='mso-ansi-language:#580A'>&nbsp;</span></p>

<p class=MsoNormal><span lang=es-419 style='mso-ansi-language:#580A'>&nbsp;</span></p>

<table class=MsoNormalTable border=0 cellspacing=0 cellpadding=0
 style='border-collapse:collapse;mso-yfti-tbllook:1184;mso-padding-alt:0cm 0cm 0cm 0cm'>
 <tr style='mso-yfti-irow:0;mso-yfti-firstrow:yes'>
  <td width=147 valign=top style='width:110.35pt;border:solid #999999 1.0pt;
  border-bottom:solid #666666 1.5pt;padding:0cm 5.4pt 0cm 5.4pt'>
  <p class=MsoNormal style='margin-bottom:0cm;line-height:normal'><b><span
  lang=es-419 style='mso-ansi-language:#580A'>Análisis de clúster</span></b></p>
  </td>
  <td width=147 valign=top style='width:110.35pt;border-top:solid #999999 1.0pt;
  border-left:none;border-bottom:solid #666666 1.5pt;border-right:solid #999999 1.0pt;
  padding:0cm 5.4pt 0cm 5.4pt'>
  <p class=MsoNormal style='margin-bottom:0cm;line-height:normal'><b><span
  lang=es-419 style='mso-ansi-language:#580A'>Técnicas disponibles </span></b></p>
  </td>
  <td width=147 valign=top style='width:110.35pt;border-top:solid #999999 1.0pt;
  border-left:none;border-bottom:solid #666666 1.5pt;border-right:solid #999999 1.0pt;
  padding:0cm 5.4pt 0cm 5.4pt'>
  <p class=MsoNormal style='margin-bottom:0cm;line-height:normal'><b><span
  lang=es-419 style='mso-ansi-language:#580A'>Ventajas </span></b></p>
  </td>
  <td width=147 valign=top style='width:110.35pt;border-top:solid #999999 1.0pt;
  border-left:none;border-bottom:solid #666666 1.5pt;border-right:solid #999999 1.0pt;
  padding:0cm 5.4pt 0cm 5.4pt'>
  <p class=MsoNormal style='margin-bottom:0cm;line-height:normal'><b><span
  lang=es-419 style='mso-ansi-language:#580A'>Desventajas</span></b></p>
  </td>
 </tr>
 <tr style='mso-yfti-irow:1;height:69.95pt'>
  <td width=147 valign=top style='width:110.35pt;border:solid #999999 1.0pt;
  border-top:none;padding:0cm 5.4pt 0cm 5.4pt;height:69.95pt'>
  <p class=MsoNormal style='margin-bottom:0cm;line-height:normal'><b><span
  lang=es-419 style='mso-ansi-language:#580A'>Jerárquico</span></b></p>
  <p class=MsoNormal style='margin-bottom:0cm;line-height:normal'><b><span
  lang=es-419 style='mso-ansi-language:#580A'>Une y a la vez divide en base a
  las características que poseen los métodos a aplicarse, eso <span
  class=SpellE>si</span> que si se agrega una unidad a un grupo esta no se
  puede deshacer porque solo puede ser asignada una sola vez.</span></b></p>
  </td>
  <td width=147 valign=top style='width:110.35pt;border-top:none;border-left:
  none;border-bottom:solid #999999 1.0pt;border-right:solid #999999 1.0pt;
  padding:0cm 5.4pt 0cm 5.4pt;height:69.95pt'>
  <p class=MsoListParagraph style='margin-bottom:0cm;text-indent:-18.0pt;
  line-height:normal;mso-list:l0 level1 lfo1'><![if !supportLists]><span
  style='font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:
  Symbol'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
  </span></span></span><![endif]><span lang=es-419 style='mso-ansi-language:
  #580A'>Aglomeración <span class=SpellE>aglomerativa</span>.</span></p>
  <p class=MsoListParagraph style='margin-bottom:0cm;text-indent:-18.0pt;
  line-height:normal;mso-list:l0 level1 lfo1'><![if !supportLists]><span
  style='font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:
  Symbol'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
  </span></span></span><![endif]><span class=SpellE><span lang=es-419
  style='mso-ansi-language:#580A'>Dendograma</span></span><span lang=es-419
  style='mso-ansi-language:#580A'>.</span></p>
  <p class=MsoListParagraph style='margin-bottom:0cm;text-indent:-18.0pt;
  line-height:normal;mso-list:l0 level1 lfo1'><![if !supportLists]><span
  style='font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:
  Symbol'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
  </span></span></span><![endif]><span lang=es-419 style='mso-ansi-language:
  #580A'>Mapa de calor.</span></p>
  </td>
  <td width=147 valign=top style='width:110.35pt;border-top:none;border-left:
  none;border-bottom:solid #999999 1.0pt;border-right:solid #999999 1.0pt;
  padding:0cm 5.4pt 0cm 5.4pt;height:69.95pt'>
  <p class=MsoNormal style='margin-bottom:0cm;line-height:normal'><span
  lang=es-419 style='mso-ansi-language:#580A'>Explorar diversas estructuras
  jerárquicas, no requiere especificar el número de clúster, perfecto para
  conjunto de datos pequeños.</span></p>
  </td>
  <td width=147 valign=top style='width:110.35pt;border-top:none;border-left:
  none;border-bottom:solid #999999 1.0pt;border-right:solid #999999 1.0pt;
  padding:0cm 5.4pt 0cm 5.4pt;height:69.95pt'>
  <p class=MsoNormal style='margin-bottom:0cm;line-height:normal'><span
  lang=es-419 style='mso-ansi-language:#580A'>La interpretación del diagrama,
  las asignaciones de agrupamiento son secuenciales, errores que pueden
  propagarse y afectar la solución final</span></p>
  </td>
 </tr>
 <tr style='mso-yfti-irow:2;mso-yfti-lastrow:yes;height:4.0cm'>
  <td width=147 valign=top style='width:110.35pt;border:solid #999999 1.0pt;
  border-top:none;padding:0cm 5.4pt 0cm 5.4pt;height:4.0cm'>
  <p class=MsoNormal style='margin-bottom:0cm;line-height:normal'><b><span
  lang=es-419 style='mso-ansi-language:#580A'>No jerárquico</span></b></p>
  <p class=MsoNormal style='margin-bottom:0cm;line-height:normal'><b><span
  lang=es-419 style='mso-ansi-language:#580A'>Clasifican las observaciones en
  un conjunto de datos, estos se asignan de acuerdo a los parentescos o cosas
  en común que posean. </span></b></p>
  </td>
  <td width=147 valign=top style='width:110.35pt;border-top:none;border-left:
  none;border-bottom:solid #999999 1.0pt;border-right:solid #999999 1.0pt;
  padding:0cm 5.4pt 0cm 5.4pt;height:4.0cm'>
  <p class=MsoListParagraph style='margin-bottom:0cm;text-indent:-18.0pt;
  line-height:normal;mso-list:l1 level1 lfo2'><![if !supportLists]><span
  style='font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:
  Symbol'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
  </span></span></span><![endif]><span lang=es-419 style='mso-ansi-language:
  #580A'>Agrupamiento de las k-medias.</span></p>
  <p class=MsoListParagraph style='margin-bottom:0cm;text-indent:-18.0pt;
  line-height:normal;mso-list:l1 level1 lfo2'><![if !supportLists]><span
  style='font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:
  Symbol'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
  </span></span></span><![endif]><span lang=es-419 style='mso-ansi-language:
  #580A'>Algoritmo PAM.</span></p>
  <p class=MsoListParagraph style='margin-bottom:0cm;text-indent:-18.0pt;
  line-height:normal;mso-list:l1 level1 lfo2'><![if !supportLists]><span
  style='font-family:Symbol;mso-fareast-font-family:Symbol;mso-bidi-font-family:
  Symbol'><span style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
  </span></span></span><![endif]><span lang=es-419 style='mso-ansi-language:
  #580A'>CLARA.</span></p>
  <p class=MsoNormal style='margin-bottom:0cm;line-height:normal'><span
  lang=es-419 style='mso-ansi-language:#580A'>&nbsp;</span></p>
  </td>
  <td width=147 valign=top style='width:110.35pt;border-top:none;border-left:
  none;border-bottom:solid #999999 1.0pt;border-right:solid #999999 1.0pt;
  padding:0cm 5.4pt 0cm 5.4pt;height:4.0cm'>
  <p class=MsoNormal style='margin-bottom:0cm;line-height:normal'><span
  lang=es-419 style='mso-ansi-language:#580A'>Calcular k-medias para un rango
  de k valores, el ritmo de algoritmos de las k-<span class=SpellE>means</span>
  varias veces y el PAM que es un valor poco sensible a los atípicos.</span></p>
  </td>
  <td width=147 valign=top style='width:110.35pt;border-top:none;border-left:
  none;border-bottom:solid #999999 1.0pt;border-right:solid #999999 1.0pt;
  padding:0cm 5.4pt 0cm 5.4pt;height:4.0cm'>
  <p class=MsoNormal style='margin-bottom:0cm;line-height:normal'><span
  lang=es-419 style='mso-ansi-language:#580A'>Los resultados finales son
  sensibles a la selección de los aleatoria inicial de los datos, también a los
  valores atípicos y por otro lado si reorganizas los datos puedes obtener una
  solución diferente.</span></p>
  </td>
 </tr>
</table>

<p class=MsoNormal><span lang=es-419 style='mso-ansi-language:#580A'>&nbsp;</span></p>

<p class=MsoNormal><span lang=es-419 style='mso-ansi-language:#580A'>&nbsp;</span></p>

<p class=MsoNormal><span lang=es-419 style='mso-ansi-language:#580A'>&nbsp;</span></p>

</div>

</body>

</html>


Fuentes:
[Alboukadel2017]
[daysimaria2017]

# Ejercicio 4

## Capítulo 4: K-means Cluster##
```{r}
data("USArrests") # Loading the data set
df <- scale(USArrests) # Scaling the data
# View the firt 3 rows of the data
head(df, n = 3)
```

### 4.3.3 Estimación del numero de clusters ###
```{r}
library(factoextra)
fviz_nbclust(df, kmeans, method = "wss") +
geom_vline(xintercept = 4, linetype = 2)
```





Usaremos 4 clusters, k=4

### 4.3.4 Computamos K-mean ###
```{r}
# Compute k-means with k = 4
set.seed(123)
km.res <- kmeans(df, 4, nstart = 25)
print(km.res)
```




 De que querer comparar las medias de cada variable por el cluster
```{r}
aggregate(USArrests, by=list(cluster=km.res$cluster), mean)
```






Pero de querer añadir un punto de clasificacion
```{r}
dd <- cbind(USArrests, cluster = km.res$cluster)
head(dd)
```
### 4.3.5 Accedemos a los resultado de la funcion de k-mean() ###
```{r}
km.res$cluster ##Sacamos el numero de cluster para cada observacion
```
```{r}
head(km.res$cluster, 4) # Las primeras 4
```

```{r}
# Pedimos el tamaño
km.res$size
```

```{r}
# Y ahora pedimos la media
km.res$centers
```
### 4.3.6 Ahora para visualizar el cluster k-mean ###

```{r}
fviz_cluster(km.res, data = df,
palette = c("#2E9FDF", "#00AFBB", "#E7B800", "#FC4E07"),
ellipse.type = "euclid", # Concentration ellipse
star.plot = TRUE, # Add segments from centroids to items
repel = TRUE, # Avoid label overplotting (slow)
ggtheme = theme_minimal()
)
```

## Cápitulo 5: K-Medoids ##
```{r}
# Datos
data("USArrests") # Load the data set
df <- scale(USArrests) # Scale the data
head(df, n = 3) # View the firt 3 rows of the data

```

### 5.3.3 Estimamos el numero optimo de clusters ###
```{r}
library(cluster)
library(factoextra)
fviz_nbclust(df, pam, method = "silhouette")+
theme_classic()
```

### 5.3.4 Computamos el PAM clustering ###
```{r}
pam.res <- pam(df, 2)
print(pam.res)
```








 Para añadir un punto de clasificación a los datos originales
```{r}
dd <- cbind(USArrests, cluster = pam.res$cluster)
head(dd, n = 3)
```

### 5.3.5 El acceso a los resultados de la funcion pam() ###
```{r}
# Cluster medoids: New Mexico, Nebraska
pam.res$medoids
```
```{r}
# Cluster numbers
head(pam.res$clustering)
```

### 5.3.6 Visualizamos el cluster PAM ###
```{r}
fviz_cluster(pam.res,
palette = c("#00AFBB", "#FC4E07"), # color palette
ellipse.type = "t", # Concentration ellipse
repel = TRUE, # Avoid label overplotting (slow)
ggtheme = theme_classic()
)
```


## Cápitulo 6: CLARA- Conglomerando Grandes Aplicaciones ##

```{r}
# El formato de los datos y su preparacion
set.seed(1234)
# Generate 500 objects, divided into 2 clusters.
df <- rbind(cbind(rnorm(200,0,8), rnorm(200,0,8)),
cbind(rnorm(300,50,8), rnorm(300,50,8)))
# Specify column and row names
colnames(df) <- c("x", "y")

rownames(df) <- paste0("S", 1:nrow(df))
# Previewing the data
head(df, nrow = 6)
```

### 6.3.3 Estimar el número de clusters optimos ###
```{r}
library(cluster)
library(factoextra)
fviz_nbclust(df, clara, method = "silhouette")+
theme_classic()
```

### 6.3.4  Computamos CLARA ###
```{r}
# Compute CLARA
clara.res <- clara(df, 2, samples = 50, pamLike = TRUE)
# Print components of clara.res
print(clara.res)
```




Para añadir un punto de clasificacion en la data original 
```{r}
dd <- cbind(df, cluster = clara.res$cluster)
head(dd, n = 4)
```




Para acceder a los resultados regresados por clara()
```{r}
# Medoids
clara.res$medoids
```

```{r}
# Clustering
head(clara.res$clustering, 10)
```




### 6.3.5 Visualizar el Cluster CLARA ###

```{r}
fviz_cluster(clara.res,
palette = c("#00AFBB", "#FC4E07"), # color palette
ellipse.type = "t", # Concentration ellipse
geom = "point", pointsize = 1,
ggtheme = theme_classic()
)
```

## Cápitulo 7: Conglomerados Aglomerativos ##
```{r}
# Estructura de Datos y preparacion
# Load the data
data("USArrests")
# Standardize the data
df <- scale(USArrests)
# Show the first 6 rows
head(df, nrow = 6)
```

### 7.2.2 Similitud de medidas ###
```{r}
# Compute the dissimilarity matrix
# df = the standardized data
res.dist <- dist(df, method = "euclidean")
```

Para que enseñe las primeras 6 columnas y filas
```{r}
as.matrix(res.dist)[1:6, 1:6]
```

### 7.2.3 Enlazamiento ###

```{r}
res.hc <- hclust(d = res.dist, method = "ward.D2")
```


### 7.2.4 Dendogramas ###

```{r}
# cex: label size
library("factoextra")
fviz_dend(res.hc, cex = 0.5)
```

### 7.2.5 Verificamos el árbol Cluster ###
```{r}
# Compute cophentic distance
res.coph <- cophenetic(res.hc)
# Correlation between cophenetic distance and
# the original distance
cor(res.dist, res.coph)
```
```{r}
res.hc2 <- hclust(res.dist, method = "average")
cor(res.dist, cophenetic(res.hc2))
```

### 7.4 Cortamos el dendograma en diferentes partes ###

```{r}
# Cut tree into 4 groups
grp <- cutree(res.hc, k = 4)
head(grp, n = 4)
```
```{r}
# Number of members in each cluster
table(grp)
```
```{r}
# Get the names for the members of cluster 1
rownames(df)[grp == 1]
```
```{r}
# Cut in 4 groups and color by groups
fviz_dend(res.hc, k = 4, # Cut in four groups
cex = 0.5, # label size
k_colors = c("#2E9FDF", "#00AFBB", "#E7B800", "#FC4E07"),
color_labels_by_k = TRUE, # color labels by groups
rect = TRUE # Add rectangle around groups
)
```








Pero de preferirlo con el diagrama de dispersion usamos la función fviz_cluster()
```{r}
fviz_cluster(list(data = df, cluster = grp),
palette = c("#2E9FDF", "#00AFBB", "#E7B800", "#FC4E07"),
ellipse.type = "convex", # Concentration ellipse
repel = TRUE, # Avoid label overplotting (slow)
show.clust.cent = FALSE, ggtheme = theme_minimal())
```

## Capitulo 8: Comparación de Dendogramas ##


```{r}
# Preparación de los datos
df <- scale(USArrests)
# Subset containing 10 rows
set.seed(123)
ss <- sample(1:50, 10)
df <- df[ss,]
```



### 8.2 Comparando dendogramas ###
```{r}
library(dendextend)
# Compute distance matrix
res.dist <- dist(df, method = "euclidean")
# Compute 2 hierarchical clusterings
hc1 <- hclust(res.dist, method = "average")
hc2 <- hclust(res.dist, method = "ward.D2")
# Create two dendrograms
dend1 <- as.dendrogram (hc1)
dend2 <- as.dendrogram (hc2)
# Create a list to hold dendrograms
dend_list <- dendlist(dend1, dend2)
```

### 8.2.1 Comparación visual de los 2 dendogramas ###
```{r}
tanglegram(dend1, dend2)
```







 Se puede costumizar el tanglegrama
```{r}
tanglegram(dend1, dend2,
highlight_distinct_edges = FALSE, # Turn-off dashed lines
common_subtrees_color_lines = FALSE, # Turn-off line colors
common_subtrees_color_branches = TRUE, # Color common branches
main = paste("entanglement =", round(entanglement(dend_list), 2))
)
```


### 8.2.2 La correlacion matriz entre una lista de dendogramas ###


Hay 2 métodos para la matriz de correlación entre las listas de arboles. El método Cophenetic y el metodo Baker
```{r}
# Cophenetic correlation matrix
cor.dendlist(dend_list, method = "cophenetic")
```
```{r}
# Baker correlation matrix
cor.dendlist(dend_list, method = "baker")
```







La Correlacion entre ambos se puede hacer de 2 maneras
```{r}
# Cophenetic correlation coefficient
cor_cophenetic(dend1, dend2)
```
```{r}
# Baker correlation coefficient
cor_bakers_gamma(dend1, dend2)
```

Es posible la comparación de multiples dendogramas y tambien es mas sencillo para simplificar el codigo
```{r}
# Create multiple dendrograms by chaining
dend1 <- df %>% dist %>% hclust("complete") %>% as.dendrogram
dend2 <- df %>% dist %>% hclust("single") %>% as.dendrogram
dend3 <- df %>% dist %>% hclust("average") %>% as.dendrogram
dend4 <- df %>% dist %>% hclust("centroid") %>% as.dendrogram
# Compute correlation matrix
dend_list <- dendlist("Complete" = dend1, "Single" = dend2,
"Average" = dend3, "Centroid" = dend4)
cors <- cor.dendlist(dend_list)
# Print correlation matrix
round(cors, 2)
```



Para visualizar la matriz de correlacion usando el paquete corrplot 
```{r}
# Visualize the correlation matrix using corrplot package
library(corrplot)
corrplot(cors, "pie", "lower")
```


## Capítulo 9: Visualizando Dendogramas ##

```{r}
# Load data
data(USArrests)
# Compute distances and hierarchical clustering
dd <- dist(scale(USArrests), method = "euclidean")
hc <- hclust(dd, method = "ward.D2")
```


### 9.1 Visualizando Dendogramas ###


 Para crear un dendograma basico
```{r}
library(factoextra)
fviz_dend(hc, cex = 0.5)
```




 Se pueden usar los argumento de main, xlab, sub, y ylab para cambiar los nombres de los ejes
```{r}
fviz_dend(hc, cex = 0.5,
main = "Dendrogram - ward.D2",
xlab = "Eje x", ylab = "Eje y", sub = "")
```



 Para dibujar el dendograma en horizontal
```{r}

fviz_dend(hc, cex = 0.5, horiz = TRUE)
```






 De querer otro tipo de diseño también se puede usar el siguiente ejemplo 
```{r}
fviz_dend(hc, k = 4, # Cut in four groups
cex = 0.5, # label size
k_colors = c("#2E9FDF", "#00AFBB", "#E7B800", "#FC4E07"),
color_labels_by_k = TRUE, # color labels by groups
ggtheme = theme_gray() # Change theme
)
```





###De querer otros colores, se puede usar los de "journal of clinical oncology" o "jco"###
```{r}
fviz_dend(hc, cex = 0.5, k = 4, # Cut in four groups
k_colors = "jco")
```





Para dibujarlo en horizontal
```{r}
fviz_dend(hc, k = 4, cex = 0.4, horiz = TRUE, k_colors = "jco",
rect = TRUE, rect_border = "jco", rect_fill = TRUE)
```



También lo podemos dibujar como un dendograma circular
```{r}
fviz_dend(hc, cex = 0.5, k = 4,
k_colors = "jco", type = "circular")
```




Otro diseño es el estilo de árbol filogenetico 
```{r}
require("igraph")
fviz_dend(hc, k = 4, k_colors = "jco",
type = "phylogenic", repel = TRUE)
```

Para usar otro método y hacer arboles filigenticos se puede usar la libreria "igraoh"
```{r}
require("igraph")
fviz_dend(hc, k = 4, # Cut in four groups
k_colors = "jco",
type = "phylogenic", repel = TRUE,
phylo_layout = "layout.gem")
```

### 9.2 En caso de dendogramas en gran escala ###

Podemos hacer zoom a los dendogramas
```{r}
fviz_dend(hc, xlim = c(1, 20), ylim = c(1, 8))
```


### 9.2.2 Trazar un sub-árbol de Dendogramas ###
                                          
```{r}
# Create a plot of the whole dendrogram,
# and extract the dendrogram data
dend_plot <- fviz_dend(hc, k = 4, # Cut in four groups
cex = 0.5, # label size
k_colors = "jco"
)
dend_data <- attr(dend_plot, "dendrogram") # Extract dendrogram data
# Cut the dendrogram at height h = 10
dend_cuts <- cut(dend_data, h = 10)
# Visualize the truncated version containing
# two branches
fviz_dend(dend_cuts$upper)
```

```{r}
# Plot the whole dendrogram
print(dend_plot)
```

```{r}
# Plot subtree 1
fviz_dend(dend_cuts$lower[[1]], main = "Subtree 1")
# Plot subtree 2
fviz_dend(dend_cuts$lower[[2]], main = "Subtree 2")
```




También podemos trazar arboles circulares como el siguiente:
```{r}
fviz_dend(dend_cuts$lower[[2]], type = "circular")
```

### 9.2.3 Para guardar el dendograma en una pagina grande de PDF ###

```{r}
pdf("dendrogram.pdf", width=30, height=15) # Open a PDF
p <- fviz_dend(hc, k = 4, cex = 1, k_colors = "jco" ) # Do plotting
print(p)
dev.off() # Close the PDF
```

### 9.3 Manipulando dendogramas usando "dendextend" ###

Código Estándar de R para crear un dendograma
```{r}
data <- scale(USArrests)
dist.res <- dist(data)
hc <- hclust(dist.res, method = "ward.D2")
dend <- as.dendrogram(hc)
plot(dend)
```



Código de R para crear un dendograma usando operadores de cadena
```{r}
library(dendextend)
dend <- USArrests[1:5,] %>% # data
scale %>% # Scale the data
dist %>% # calculate a distance matrix,
hclust(method = "ward.D2") %>% # Hierarchical clustering
as.dendrogram # Turn the object into a dendrogram.
plot(dend)
```




# Bibliografías