Algoritmos-Tarea
Fernandez Torres Charly, Aguilar Quispe Fabricio, Chumpipuma Anthony
14/11/2019
TRABAJO DE ALGORITMOS-MINERIA DE DATOS
En esta publicación se ha trabajado los algoritmos de Kohone, Diana, Clara, Pam y Fanny. Se ha generado ejemplos de cada uno de estos algoritmos y a su vez se ha detallado cada paso a realizar para que se entienda mejor cuando se ejecuten los comandos.
#Exploracion De Datos:
1.Cargamos la base de datos que nos brindó el profesor llamada “CLIENTE”
cliente<-read.csv("https://raw.githubusercontent.com/VictorGuevaraP/Mineria-de-datos-2019-2/master/clientes.csv",sep = ";",header = T)2.Verificamos el número de filas y el número de columnas con el comando “dim(nombreBD)” de la DB “Cliente”.
dim(cliente)## [1] 850 93.Con el siguiente comando “names(nombreBD)” procederemos a verificar el nombre de cada variable o también se puede utilizar el comando “colnames(nombreBD9” de igual forma nos mostrara los nombres de cada variable.
names(cliente)## [1] "ID_cliente" "edad" "educacion"
## [4] "años_empleo" "Ingreso" "Tarjeta_credito"
## [7] "otra_tarjeta" "Direccion" "Ratio.ingreso.deuda"• DESCRIPCION DE Las Variables:
- ID_Cliente: Código asignado al cliente.
- Educación: Grado de educación alcanzada por el cliente.
- Edad: Tiempo de vida del cliente.
- Años_Empleo: Tiempo laborado por el cliente.
- Ingreso: Cantidad salarial del cliente.
- Tarjeta_Credito: Tipo de tarjeta que utiliza el cliente.
- Otra_Tarjeta: Tipo de tarjeta que utiliza el cliente.
- Direccion: Ubicación en la que reside el cliente.
- Ratio.Ingreso.Deuda: Deuda del cliente
4.Con el comando “head(nombreBD) nos mostrara las 6 primeras filas.
head(cliente)## ID_cliente edad educacion años_empleo Ingreso Tarjeta_credito
## 1 1 41 2 6 19 0.124
## 2 2 47 1 26 100 4.582
## 3 3 33 2 10 57 6.111
## 4 4 29 2 4 19 0.681
## 5 5 47 1 31 253 9.308
## 6 6 40 1 23 81 0.998
## otra_tarjeta Direccion Ratio.ingreso.deuda
## 1 1.073 NBA001 6.3
## 2 8.218 NBA021 12.8
## 3 5.802 NBA013 20.9
## 4 0.516 NBA009 6.3
## 5 8.908 NBA008 7.2
## 6 7.831 NBA016 10.95.Con el comando “tail(nombreBD) nos mostrara las 6 últimas filas.
tail(cliente)## ID_cliente edad educacion años_empleo Ingreso Tarjeta_credito
## 845 845 41 1 7 43 0.694
## 846 846 27 1 5 26 0.548
## 847 847 28 2 7 34 0.359
## 848 848 25 4 0 18 2.802
## 849 849 32 1 12 28 0.116
## 850 850 52 1 16 64 1.866
## otra_tarjeta Direccion Ratio.ingreso.deuda
## 845 1.198 NBA011 4.4
## 846 1.220 NBA007 6.8
## 847 2.021 NBA002 7.0
## 848 3.210 NBA001 33.4
## 849 0.696 NBA012 2.9
## 850 3.638 NBA025 8.66.Con el comando lapply(nombreBD,class) sabremos qué clase tiene cada variable.
lapply(cliente,class)## $ID_cliente
## [1] "integer"
##
## $edad
## [1] "integer"
##
## $educacion
## [1] "integer"
##
## $años_empleo
## [1] "integer"
##
## $Ingreso
## [1] "integer"
##
## $Tarjeta_credito
## [1] "numeric"
##
## $otra_tarjeta
## [1] "numeric"
##
## $Direccion
## [1] "factor"
##
## $Ratio.ingreso.deuda
## [1] "numeric"• ID_cliente: Es una variable de clase INTEGER.
• Edad: Es una variable de clase INTEGER.
• Educación: Es una variable de clase INTEGER.
• Años_empleo: Es una variable de clase INTEGER.
• Ingreso: Es una variable de clase INTEGER.
• Tarjeta_credito: Es una variable de clase NUMERIC.
• Otra_tarjeta: Es una variable de clase NUMERIC.
• Dirección: Es una variable de clase FACTOR.
• Ratio.ingreso.deuda: Es una variable de clase NUMERIC.
- Con el comando “summary” nos mostrara una lista detallada de cada variable identificando su primer cuartil, tercer cuartil, media, mediana, minimo y maximo:
summary(cliente)## ID_cliente edad educacion años_empleo
## Min. : 1.0 Min. :20.00 Min. :1.000 Min. : 0.000
## 1st Qu.:213.2 1st Qu.:29.00 1st Qu.:1.000 1st Qu.: 3.000
## Median :425.5 Median :34.00 Median :1.000 Median : 7.000
## Mean :425.5 Mean :35.03 Mean :1.711 Mean : 8.566
## 3rd Qu.:637.8 3rd Qu.:41.00 3rd Qu.:2.000 3rd Qu.:13.000
## Max. :850.0 Max. :56.00 Max. :5.000 Max. :33.000
##
## Ingreso Tarjeta_credito otra_tarjeta Direccion
## Min. : 13.00 Min. : 0.0120 Min. : 0.046 NBA001 : 71
## 1st Qu.: 24.00 1st Qu.: 0.3825 1st Qu.: 1.046 NBA002 : 71
## Median : 35.00 Median : 0.8850 Median : 2.003 NBA000 : 60
## Mean : 46.68 Mean : 1.5768 Mean : 3.079 NBA004 : 58
## 3rd Qu.: 55.75 3rd Qu.: 1.8985 3rd Qu.: 3.903 NBA003 : 55
## Max. :446.00 Max. :20.5610 Max. :35.197 NBA006 : 50
## (Other):485
## Ratio.ingreso.deuda
## Min. : 0.10
## 1st Qu.: 5.10
## Median : 8.70
## Mean :10.17
## 3rd Qu.:13.80
## Max. :41.30
## • La variable “ID_cliente” nos indica que tiene un mínimo de 1.0 y un máximo de 850.0, también nos indica que en su primer cuartil tiene un 213.2 y en su tercer cuartil tiene 637.8, también nos indica que media es de 425.5 y su mediana de 425.5.
• La variable “Edad” nos indica que tiene un mínimo de 20.00 y un máximo de 50.00, también nos indica que en su primer cuartil tiene un 29.00 y en su tercer cuartil tiene 41.00, también nos indica que media es de 35.03 y su mediana de 34.00.
• La variable “Educación” nos indica que tiene un mínimo de 1.000 y un máximo de 5.000, también nos indica que en su primer cuartil tiene un 1.000 y en su tercer cuartil tiene 2.000, también nos indica que media es de 1.711 y su mediana de 1.000.
• La variable “Ingreso” nos indica que tiene un mínimo de 13.00 y un máximo de 446.00, también nos indica que en su primer cuartil tiene un 24.00 y en su tercer cuartil tiene 55.75, también nos indica que media es de 46.68 y su mediana de 35.00.
• La variable “Tarjeta_credito” nos indica que tiene un mínimo de 0.0120 y un máximo de 20.5610, también nos indica que en su primer cuartil tiene un 0.3825 y en su tercer cuartil tiene 1.8985, también nos indica que media es de 1.5768 y su mediana de 0.8850.
• La variable “Otra_tarjeta” nos indica que tiene un mínimo de 0.046 y un máximo de 35.197, también nos indica que en su primer cuartil tiene un 1.046 y en su tercer cuartil tiene 3.903, también nos indica que media es de 3.079 y su mediana de 2.003.
• La variable “Ratio.ingreso.deuda” nos indica que tiene un mínimo de 0.10 y un máximo de 41.30, también nos indica que en su primer cuartil tiene un 5.10 y en su tercer cuartil tiene 13.80, también nos indica que media es de 10.17 y su mediana de 8.70.
#UNA VEZ EXPLORADO LOS DATOS PROCEDEMOS A IMPLEMENTARLO EN CADA #ALGORITMO BRINDADO POR EL PROFESOR EN CLASE.
1. ALGORITMO DE KOHONE
El primer paso a realizar es cargar la data a trabajar.
cliente<-read.csv("https://raw.githubusercontent.com/VictorGuevaraP/Mineria-de-datos-2019-2/master/clientes.csv",sep = ";",header = T)Ahora continuamos a hacer un summary de la data para detectar si existen NA’s. Se separa la data en ttesting, el cual llevará mas datos, y taprendizaje, luego visualizaremos las variables de la data.
summary(cliente)## ID_cliente edad educacion años_empleo
## Min. : 1.0 Min. :20.00 Min. :1.000 Min. : 0.000
## 1st Qu.:213.2 1st Qu.:29.00 1st Qu.:1.000 1st Qu.: 3.000
## Median :425.5 Median :34.00 Median :1.000 Median : 7.000
## Mean :425.5 Mean :35.03 Mean :1.711 Mean : 8.566
## 3rd Qu.:637.8 3rd Qu.:41.00 3rd Qu.:2.000 3rd Qu.:13.000
## Max. :850.0 Max. :56.00 Max. :5.000 Max. :33.000
##
## Ingreso Tarjeta_credito otra_tarjeta Direccion
## Min. : 13.00 Min. : 0.0120 Min. : 0.046 NBA001 : 71
## 1st Qu.: 24.00 1st Qu.: 0.3825 1st Qu.: 1.046 NBA002 : 71
## Median : 35.00 Median : 0.8850 Median : 2.003 NBA000 : 60
## Mean : 46.68 Mean : 1.5768 Mean : 3.079 NBA004 : 58
## 3rd Qu.: 55.75 3rd Qu.: 1.8985 3rd Qu.: 3.903 NBA003 : 55
## Max. :446.00 Max. :20.5610 Max. :35.197 NBA006 : 50
## (Other):485
## Ratio.ingreso.deuda
## Min. : 0.10
## 1st Qu.: 5.10
## Median : 8.70
## Mean :10.17
## 3rd Qu.:13.80
## Max. :41.30
## muestra <- sample(1:200,75)
ttesting <- cliente[muestra,]
taprendizaje <- cliente[-muestra,]
head(taprendizaje)## ID_cliente edad educacion años_empleo Ingreso Tarjeta_credito
## 1 1 41 2 6 19 0.124
## 3 3 33 2 10 57 6.111
## 4 4 29 2 4 19 0.681
## 6 6 40 1 23 81 0.998
## 8 8 42 3 0 64 0.279
## 9 9 26 1 5 18 0.575
## otra_tarjeta Direccion Ratio.ingreso.deuda
## 1 1.073 NBA001 6.3
## 3 5.802 NBA013 20.9
## 4 0.516 NBA009 6.3
## 6 7.831 NBA016 10.9
## 8 3.945 NBA009 6.6
## 9 2.215 NBA006 15.5Antes de crear un SOM, debemos elegir las variables en las que queremos buscar patrones
colnames(cliente)## [1] "ID_cliente" "edad" "educacion"
## [4] "años_empleo" "Ingreso" "Tarjeta_credito"
## [7] "otra_tarjeta" "Direccion" "Ratio.ingreso.deuda"Comenzaremos con algunos ejemplos simples usando intentos de disparo de esta manera podremos tener resultados con los que se podrán trabajar después.
library(kohonen)
cliente.measures1 <- c("edad", "años_empleo", "Ingreso")
cliente.SOM1 <- som(scale(cliente[cliente.measures1]), grid = somgrid(6, 4, "rectangular"))
plot(cliente.SOM1)
Mapa de calor SOM Recuerde que lo anterior es solo un mapa de los datos del cliente: cada celda muestra su vector representativo. Podríamos identificar clientes con celdas en el mapa asignando a cada cliente a la celda con el vector representativo más cercano a la línea estadística de ese cliente El tipo SOM de “conteo” hace exactamente esto, y crea un mapa de calor basado en la cantidad de cliente asignados a cada celda. Solo por diversión, invertimos el orden de la paleta predefinida heat.colorspara que el rojo represente las celdas de la cuadrícula con un mayor número de cliente representados. reverse color ramp
colors <- function(n, alpha = 1) {
rev(heat.colors(n, alpha))
}
plot(cliente.SOM1, type = "counts", palette.name = colors, heatkey = TRUE)
par(mfrow = c(1, 2))
plot(cliente.SOM1, type = "mapping", pchs = 20, main = "Mapping Type SOM")
plot(cliente.SOM1, main = "Default SOM Plot")
cliente.SOM2 <- som(scale(cliente[cliente.measures1]), grid = somgrid(6, 6, "hexagonal"))
par(mfrow = c(1, 2))
plot(cliente.SOM2, type = "mapping", pchs = 20, main = "Mapping Type SOM")
plot(cliente.SOM2, main = "Default SOM Plot")
plot(cliente.SOM2, type = "dist.neighbours", palette.name = terrain.colors)
#SOM supervisados El kohonenpaquete también admite SOM supervisados, lo que nos permite hacer clasificaciones. Hasta ahora solo hemos trabajado con el mapeo de datos tridimensionales a dos dimensiones. La utilidad de los SOM se vuelve más evidente cuando trabajamos con datos dimensionales más altos, así que hagamos este ejemplo supervisado con una lista ampliada de estadísticas de clientes:
colnames(cliente)## [1] "ID_cliente" "edad" "educacion"
## [4] "años_empleo" "Ingreso" "Tarjeta_credito"
## [7] "otra_tarjeta" "Direccion" "Ratio.ingreso.deuda"cliente.measures1 <- c("edad", "años_empleo", "Ingreso")
training_indices <- sample(nrow(cliente), 150)
cliente.training <- scale(cliente[training_indices, cliente.measures1])
cliente.testing <- scale(cliente[-training_indices, cliente.measures1])
summary(cliente.testing)## edad años_empleo Ingreso
## Min. :-1.8497 Min. :-1.2357 Min. :-0.8498
## 1st Qu.:-0.8586 1st Qu.:-0.7993 1st Qu.:-0.5742
## Median :-0.1152 Median :-0.2175 Median :-0.3237
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.6281 3rd Qu.: 0.6551 3rd Qu.: 0.2086
## Max. : 2.6104 Max. : 3.5640 Max. : 9.9977Tenga en cuenta que cuando cambiamos la escala de nuestros datos de prueba, debemos escalar de acuerdo con la escala de nuestros datos de capacitación.
cliente.SOM3 <- xyf(cliente.training, classvec2classmat(cliente$Ingreso[training_indices]),
grid = somgrid(6, 6, "hexagonal"), rlen = 100)
summary(cliente.SOM3)## SOM of size 6x6 with a hexagonal topology and a bubble neighbourhood function.
## The number of data layers is 2.
## Distance measure(s) used: sumofsquares, tanimoto.
## Training data included: 150 objects.
## Mean distance to the closest unit in the map: 0.015.2. Algoritmo DIANA
Importamos la BD clientes con la cual se va a trabajar.
Cliente<-read.csv("https://raw.githubusercontent.com/VictorGuevaraP/Mineria-de-datos-2019-2/master/clientes.csv",sep = ";")En esta parte se decide con que matriz trabajar. Se decidió trabajar con la matriz de distancia euclidean.
mat_dist<-dist(x = Cliente, method = "euclidean")Dendogramas con linkage complete y average para que nos muestre el porcentaje de cada una de ellas.
hc_euc_complete<-hclust(d = mat_dist, method = "complete")
hc_euc_average<-hclust(d = mat_dist, method = "average")
cor(x = mat_dist, cophenetic(hc_euc_complete))## [1] 0.7375627cor(x = mat_dist, cophenetic(hc_euc_average))## [1] 0.7310714Se simulan datos aleatorios con dos dimensiones, a su vez se hace el cambio del tipo de dato.
set.seed(111)
Cliente<-matrix(rnorm(n = 100*2),nrow = 100, ncol = 2,
dimnames = list(NULL,c("x","y")))
Cliente<-as.data.frame(Cliente)Dendogramas con linkage complete y average
hc_euc_complete<-hclust(d = mat_dist, method = "complete")
hc_euc_average<-hclust(d = mat_dist, method = "average")
cor(x = mat_dist, cophenetic(hc_euc_complete))## [1] 0.7375627cor(x = mat_dist, cophenetic(hc_euc_average))## [1] 0.73107143. Algoritmo PAM
Importamos la BD clientes con la cual trabajaremos.
Cliente<-read.csv("https://raw.githubusercontent.com/VictorGuevaraP/Mineria-de-datos-2019-2/master/clientes.csv",sep = ";")Eliminamos la columna ID_CLIENTE porque no es un valor relevante con lo que queremos hacer también eliminamos la columna dirección porque no es un tipo de dato numérico y nos impide realiar nuestro cluestering.
str(Cliente)## 'data.frame': 850 obs. of 9 variables:
## $ ID_cliente : int 1 2 3 4 5 6 7 8 9 10 ...
## $ edad : int 41 47 33 29 47 40 38 42 26 47 ...
## $ educacion : int 2 1 2 2 1 1 2 3 1 3 ...
## $ años_empleo : int 6 26 10 4 31 23 4 0 5 23 ...
## $ Ingreso : int 19 100 57 19 253 81 56 64 18 115 ...
## $ Tarjeta_credito : num 0.124 4.582 6.111 0.681 9.308 ...
## $ otra_tarjeta : num 1.073 8.218 5.802 0.516 8.908 ...
## $ Direccion : Factor w/ 32 levels "NBA000","NBA001",..: 2 22 14 10 9 17 14 10 7 12 ...
## $ Ratio.ingreso.deuda: num 6.3 12.8 20.9 6.3 7.2 10.9 1.6 6.6 15.5 4 ...Cliente$ID_cliente<-NULL
Cliente$Direccion<-NULLLuego escalamos los datos para normalizar la data de esta manera podremos tenerla mas entendible
cliepam <- scale(Cliente)
library(cluster)
library(factoextra)Con este gráfico de codo observamos que lo óptimo sería crear dos cluestering, pero optamos crear 3 porque de esta manera ganamos un poco mas de eficiencia.
fviz_nbclust(x = Cliente, FUNcluster = pam, method = "wss", k.max =15,
diss = dist(Cliente, method = "manhattan"))
set.seed(111)
pam_clusters <- pam(Cliente, k = 3, metric = "manhattan")
pam_clusters## Medoids:
## ID edad educacion años_empleo Ingreso Tarjeta_credito otra_tarjeta
## [1,] 531 29 1 4 24 0.867 1.005
## [2,] 474 41 1 19 96 2.254 5.234
## [3,] 499 37 2 11 47 1.597 2.915
## Ratio.ingreso.deuda
## [1,] 7.8
## [2,] 7.8
## [3,] 9.6
## Clustering vector:
## [1] 1 2 3 1 2 2 3 3 1 2 2 3 1 1 1 1 1 1 3 3 1 3 3 2 2 3 1 3 3 3 2 3 1 1 3
## [36] 1 1 1 1 2 1 2 1 2 1 2 1 1 1 3 2 3 1 2 3 1 1 1 3 3 3 3 3 2 1 3 2 1 1 1
## [71] 3 3 2 3 1 1 3 1 2 3 3 3 2 3 3 1 1 1 1 1 3 1 1 1 1 3 1 3 1 1 1 2 3 3 1
## [106] 3 1 1 3 2 3 1 3 1 1 3 1 3 3 1 1 1 1 3 3 3 3 1 1 1 3 3 1 1 1 1 2 1 3 3
## [141] 3 1 3 1 3 1 3 3 1 1 3 3 1 1 3 3 2 2 1 3 1 3 2 3 1 1 3 1 1 3 2 1 1 3 1
## [176] 1 3 1 1 2 3 3 1 2 1 3 1 1 3 3 1 3 2 1 2 1 1 3 2 1 2 1 1 1 1 3 1 2 2 1
## [211] 1 3 3 2 1 1 3 3 3 3 1 2 1 1 1 1 2 1 1 3 3 1 3 1 2 1 3 1 3 1 1 3 1 1 1
## [246] 2 3 3 3 1 1 1 1 1 2 3 3 1 1 1 1 2 3 3 3 3 3 3 1 3 1 1 3 3 1 1 1 3 1 1
## [281] 1 2 2 1 2 1 2 1 2 3 1 3 1 3 1 1 1 1 1 2 3 3 1 1 1 3 3 1 3 3 3 1 1 1 3
## [316] 1 1 1 3 1 2 1 2 1 1 3 1 1 2 3 3 2 1 1 3 1 1 2 1 3 3 3 1 1 2 3 1 3 1 1
## [351] 2 1 3 1 1 3 2 1 1 1 2 1 1 1 3 1 3 2 2 3 1 1 2 1 1 1 1 3 1 1 1 1 1 1 2
## [386] 1 2 1 1 1 1 3 3 3 1 3 1 2 3 1 1 1 1 1 1 3 1 3 2 1 3 3 3 1 1 3 3 1 3 1
## [421] 3 3 1 1 3 3 1 1 3 1 3 3 1 1 2 1 1 1 2 3 3 3 1 2 3 3 3 3 3 3 2 1 1 1 2
## [456] 1 1 1 1 3 3 2 1 1 1 1 2 1 3 3 2 3 3 2 3 1 3 1 1 1 3 3 3 1 1 1 3 2 1 1
## [491] 1 2 3 3 1 1 1 3 3 1 1 1 2 2 1 3 3 1 2 1 1 1 1 2 3 3 1 1 1 3 3 1 1 1 3
## [526] 1 1 3 1 1 1 1 2 2 1 3 1 1 1 3 2 3 1 3 3 2 3 1 2 1 3 2 1 2 3 1 2 1 1 1
## [561] 3 3 1 3 1 3 2 1 2 1 3 2 1 1 3 1 1 1 1 3 3 1 3 3 3 1 3 1 1 3 1 2 3 3 1
## [596] 1 1 1 2 3 3 3 1 3 1 3 1 1 1 1 1 1 1 1 3 3 3 1 3 1 2 1 1 1 1 3 1 1 3 2
## [631] 1 1 3 3 2 1 3 2 1 2 1 1 2 1 2 3 2 1 1 1 1 1 2 2 1 1 1 3 3 3 1 3 3 2 1
## [666] 3 2 1 1 3 3 3 2 1 1 2 1 3 1 3 3 1 3 2 1 3 1 3 1 1 1 3 1 3 1 3 3 1 1 1
## [701] 1 3 1 1 1 3 1 1 2 1 2 1 3 2 3 1 1 2 3 2 1 1 1 3 3 2 3 1 1 3 3 2 3 3 2
## [736] 3 1 3 3 3 3 2 1 1 3 3 2 3 1 1 1 1 1 1 1 1 1 1 2 1 2 3 1 3 1 3 3 3 1 2
## [771] 1 1 1 3 3 3 3 3 3 1 3 3 3 3 2 1 3 3 1 3 3 2 3 2 1 1 1 1 1 1 1 2 1 1 3
## [806] 1 3 3 3 1 1 1 1 3 1 1 1 1 1 3 1 3 1 1 1 2 3 1 1 1 3 1 3 1 1 3 1 1 1 1
## [841] 1 3 3 3 3 1 1 1 1 3
## Objective function:
## build swap
## 29.81590 28.18529
##
## Available components:
## [1] "medoids" "id.med" "clustering" "objective" "isolation"
## [6] "clusinfo" "silinfo" "diss" "call" "data"FINALMENTE PODEMOS OBSERVAR LOS 3 CLUESTERING CREADOS POR EL METODO PAM.
fviz_cluster(object = pam_clusters, data = Cliente) +
theme_bw() +
labs(title = "Resultados Cluestering PAM") +
theme(legend.position = "none")
4. ALGORITMO CLARA:
Cargamos la DATA “Clientes” y luego procederemos a hacer las siguientes operaciones
cliente<-read.csv("https://raw.githubusercontent.com/VictorGuevaraP/Mineria-de-datos-2019-2/master/clientes.csv", sep = ";")Para ilustrar la aplicación del método CLARA se simula un set de datos bidimensional (dos variables) con 500 observaciones, de las cuales 200 pertenecen a un grupo y 300 a otro (número de grupos reales = 2).
set.seed(1234)
colnames(cliente) <- c("x", "y")
head(cliente)## x y NA NA NA NA NA NA NA
## 1 1 41 2 6 19 0.124 1.073 NBA001 6.3
## 2 2 47 1 26 100 4.582 8.218 NBA021 12.8
## 3 3 33 2 10 57 6.111 5.802 NBA013 20.9
## 4 4 29 2 4 19 0.681 0.516 NBA009 6.3
## 5 5 47 1 31 253 9.308 8.908 NBA008 7.2
## 6 6 40 1 23 81 0.998 7.831 NBA016 10.9##Usaremos estas dos librerías que nos ayudarán a hacer el algorítmo Clara
library(cluster)
library(factoextra)una matriz numérica x donde cada fila es una observación, el número de clusters k, la medida de distancia empleada metric (euclídea o manhattan), si los datos se tienen que estandarizar stand, el número de partes samples en las que se divide el set de datos es de 50 y si se utiliza el algoritmo PAM pamLike.
clara_clustersFA<- clara(x = cliente, k = 2, metric = "manhattan", stand = TRUE,
samples = 50, pamLike = TRUE)
clara_clustersFA## Call: clara(x = cliente, k = 2, metric = "manhattan", stand = TRUE, samples = 50, pamLike = TRUE)
## Medoids:
## x y <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## [1,] 350 34 1 5 33 0.778 1.631 4 7.3
## [2,] 499 37 2 11 47 1.597 2.915 12 9.6
## Objective function: 7.671177
## Clustering vector: int [1:850] 1 2 2 1 2 2 2 2 1 2 2 1 1 1 1 1 1 1 ...
## Cluster sizes: 474 376
## Best sample:
## [1] 3 13 79 87 92 114 115 198 208 233 234 268 299 350 368 376 394
## [18] 400 444 445 448 466 472 499 540 542 554 563 573 601 603 609 625 641
## [35] 644 648 668 676 717 738 756 758 771 787
##
## Available components:
## [1] "sample" "medoids" "i.med" "clustering" "objective"
## [6] "clusinfo" "diss" "call" "silinfo" "data"En ésta parte se muestra los resultados de nuestro clustering
fviz_cluster(object = clara_clustersFA, ellipse.type = "t", geom = "point",
pointsize = 2.5) +
theme_bw() +
labs(title = "Resultados clustering CLARA") +
theme(legend.position = "none")
plot(silhouette(clara_clustersFA), col = 2:3, main = "Silhouette plot")
fviz_cluster(clara_clustersFA)
fviz_silhouette(clara_clustersFA)## cluster size ave.sil.width
## 1 1 23 0.49
## 2 2 21 -0.05
5. ALGORITMO DE fanny
Cargamos la DATA “Clientes” y luego procederemos a hacer las siguientes operaciones
cliente<-read.csv("https://raw.githubusercontent.com/VictorGuevaraP/Mineria-de-datos-2019-2/master/clientes.csv", sep = ";")Eliminamos la columna direccion
cliente$Direccion<-NULL
summary(cliente)## ID_cliente edad educacion años_empleo
## Min. : 1.0 Min. :20.00 Min. :1.000 Min. : 0.000
## 1st Qu.:213.2 1st Qu.:29.00 1st Qu.:1.000 1st Qu.: 3.000
## Median :425.5 Median :34.00 Median :1.000 Median : 7.000
## Mean :425.5 Mean :35.03 Mean :1.711 Mean : 8.566
## 3rd Qu.:637.8 3rd Qu.:41.00 3rd Qu.:2.000 3rd Qu.:13.000
## Max. :850.0 Max. :56.00 Max. :5.000 Max. :33.000
## Ingreso Tarjeta_credito otra_tarjeta Ratio.ingreso.deuda
## Min. : 13.00 Min. : 0.0120 Min. : 0.046 Min. : 0.10
## 1st Qu.: 24.00 1st Qu.: 0.3825 1st Qu.: 1.046 1st Qu.: 5.10
## Median : 35.00 Median : 0.8850 Median : 2.003 Median : 8.70
## Mean : 46.68 Mean : 1.5768 Mean : 3.079 Mean :10.17
## 3rd Qu.: 55.75 3rd Qu.: 1.8985 3rd Qu.: 3.903 3rd Qu.:13.80
## Max. :446.00 Max. :20.5610 Max. :35.197 Max. :41.30head(cliente)## ID_cliente edad educacion años_empleo Ingreso Tarjeta_credito
## 1 1 41 2 6 19 0.124
## 2 2 47 1 26 100 4.582
## 3 3 33 2 10 57 6.111
## 4 4 29 2 4 19 0.681
## 5 5 47 1 31 253 9.308
## 6 6 40 1 23 81 0.998
## otra_tarjeta Ratio.ingreso.deuda
## 1 1.073 6.3
## 2 8.218 12.8
## 3 5.802 20.9
## 4 0.516 6.3
## 5 8.908 7.2
## 6 7.831 10.9plot(cliente)
Escojer cluster necesarios Metodo de Slope
wss=as.numeric()
for (k in 2:10) {
agrupa=kmeans(cliente, k)
wss[k-1]= agrupa$tot.withinss
}
plot(2:10, wss, type = "b")
library(cluster)
cliente_agrupa=fanny(x = cliente, diss = FALSE, k = 3, metric = "euclidean", stand = FALSE)
cliente_agrupa## Fuzzy Clustering object of class 'fanny' :
## m.ship.expon. 2
## objective 31231.79
## tolerance 1e-15
## iterations 51
## converged 1
## maxit 500
## n 850
## Membership coefficients (in %, rounded):
## [,1] [,2] [,3]
## [1,] 71 19 10
## [2,] 69 20 11
## [3,] 71 18 10
## [4,] 71 18 10
## [5,] 55 28 17
## [6,] 71 19 10
## [7,] 72 18 10
## [8,] 72 18 10
## [9,] 72 18 10
## [10,] 68 20 11
## [11,] 71 19 10
## [12,] 73 17 10
## [13,] 72 18 10
## [14,] 73 17 10
## [15,] 73 17 10
## [16,] 73 18 10
## [17,] 73 17 10
## [18,] 74 17 9
## [19,] 74 17 9
## [20,] 74 17 9
## [21,] 74 17 9
## [22,] 75 16 9
## [23,] 75 16 9
## [24,] 73 17 9
## [25,] 69 20 11
## [26,] 75 16 9
## [27,] 75 16 9
## [28,] 76 16 9
## [29,] 75 16 9
## [30,] 76 16 8
## [31,] 72 18 10
## [32,] 76 16 8
## [33,] 76 16 8
## [34,] 76 15 8
## [35,] 77 15 8
## [36,] 77 15 8
## [37,] 77 15 8
## [38,] 76 15 8
## [39,] 76 15 8
## [40,] 70 19 10
## [41,] 76 15 8
## [42,] 72 18 10
## [43,] 78 15 8
## [44,] 64 23 13
## [45,] 77 15 8
## [46,] 76 16 8
## [47,] 77 15 8
## [48,] 78 14 7
## [49,] 78 14 8
## [50,] 79 14 7
## [51,] 77 15 8
## [52,] 78 14 7
## [53,] 78 14 7
## [54,] 78 15 8
## [55,] 78 14 7
## [56,] 80 13 7
## [57,] 79 14 7
## [58,] 80 13 7
## [59,] 80 13 7
## [60,] 80 13 7
## [61,] 80 13 7
## [62,] 81 13 6
## [63,] 80 13 7
## [64,] 79 14 7
## [65,] 80 13 7
## [66,] 82 12 6
## [67,] 79 14 7
## [68,] 82 12 6
## [69,] 81 12 6
## [70,] 80 13 7
## [71,] 82 12 6
## [72,] 80 13 7
## [73,] 77 15 8
## [74,] 83 12 6
## [75,] 83 11 6
## [76,] 82 12 6
## [77,] 83 11 6
## [78,] 82 12 6
## [79,] 69 20 11
## [80,] 83 12 6
## [81,] 82 12 6
## [82,] 81 13 6
## [83,] 67 21 11
## [84,] 84 11 5
## [85,] 83 11 5
## [86,] 82 12 6
## [87,] 83 11 5
## [88,] 83 11 6
## [89,] 85 10 5
## [90,] 84 11 5
## [91,] 82 12 6
## [92,] 85 10 5
## [93,] 84 11 5
## [94,] 84 11 5
## [95,] 85 10 5
## [96,] 85 10 5
## [97,] 85 10 5
## [98,] 85 10 5
## [99,] 85 10 5
## [100,] 84 11 5
## [101,] 85 10 5
## [102,] 73 18 9
## [103,] 85 10 5
## [104,] 86 10 5
## [105,] 85 10 5
## [106,] 84 11 5
## [107,] 86 9 4
## [108,] 86 10 5
## [109,] 86 10 4
## [110,] 82 12 6
## [111,] 86 9 4
## [112,] 86 10 4
## [113,] 87 9 4
## [114,] 84 11 5
## [115,] 86 9 4
## [116,] 87 9 4
## [117,] 85 10 5
## [118,] 87 9 4
## [119,] 85 10 5
## [120,] 87 9 4
## [121,] 86 10 4
## [122,] 87 9 4
## [123,] 87 9 4
## [124,] 86 9 4
## [125,] 87 9 4
## [126,] 84 11 5
## [127,] 87 9 4
## [128,] 86 10 4
## [129,] 85 10 5
## [130,] 85 11 5
## [131,] 87 9 4
## [132,] 86 10 4
## [133,] 86 9 4
## [134,] 86 10 4
## [135,] 86 10 4
## [136,] 87 9 4
## [137,] 80 14 6
## [138,] 85 10 5
## [139,] 86 10 4
## [140,] 86 9 4
## [141,] 86 9 4
## [142,] 85 11 5
## [143,] 86 9 4
## [144,] 85 10 4
## [145,] 83 12 5
## [146,] 85 10 4
## [147,] 85 11 5
## [148,] 85 10 4
## [149,] 85 10 4
## [150,] 85 11 5
## [151,] 85 10 4
## [152,] 84 11 5
## [153,] 85 10 4
## [154,] 83 12 5
## [155,] 83 12 5
## [156,] 85 11 5
## [157,] 73 19 8
## [158,] 75 17 7
## [159,] 83 12 5
## [160,] 83 12 5
## [161,] 82 12 5
## [162,] 84 11 5
## [163,] 79 15 6
## [164,] 84 12 5
## [165,] 82 12 5
## [166,] 83 12 5
## [167,] 82 12 5
## [168,] 82 13 5
## [169,] 82 13 5
## [170,] 82 13 5
## [171,] 76 17 7
## [172,] 81 14 6
## [173,] 81 13 5
## [174,] 82 13 5
## [175,] 81 14 5
## [176,] 78 15 6
## [177,] 80 14 6
## [178,] 80 15 6
## [179,] 81 14 6
## [180,] 74 19 8
## [181,] 80 14 6
## [182,] 80 14 6
## [183,] 78 16 6
## [184,] 65 25 10
## [185,] 76 17 7
## [186,] 78 16 6
## [187,] 77 16 6
## [188,] 78 16 6
## [189,] 78 16 6
## [190,] 75 18 7
## [191,] 77 17 6
## [192,] 76 18 7
## [193,] 74 19 7
## [194,] 76 17 7
## [195,] 74 19 7
## [196,] 76 17 7
## [197,] 74 19 7
## [198,] 73 20 7
## [199,] 51 33 16
## [200,] 74 19 7
## [201,] 65 25 10
## [202,] 73 20 7
## [203,] 73 20 7
## [204,] 73 20 7
## [205,] 72 20 7
## [206,] 72 20 7
## [207,] 72 20 7
## [208,] 49 34 17
## [209,] 66 25 9
## [210,] 71 22 8
## [211,] 71 22 8
## [212,] 71 22 8
## [213,] 70 22 8
## [214,] 67 24 9
## [215,] 69 23 8
## [216,] 68 24 8
## [217,] 68 24 8
## [218,] 67 24 9
## [219,] 68 24 8
## [220,] 67 25 9
## [221,] 66 25 9
## [222,] 60 29 11
## [223,] 67 25 8
## [224,] 66 26 9
## [225,] 66 26 9
## [226,] 65 26 9
## [227,] 60 29 11
## [228,] 65 26 9
## [229,] 64 27 9
## [230,] 64 27 9
## [231,] 64 27 9
## [232,] 63 28 9
## [233,] 63 28 9
## [234,] 62 29 10
## [235,] 60 30 10
## [236,] 61 29 10
## [237,] 61 29 9
## [238,] 60 30 10
## [239,] 60 30 10
## [240,] 60 30 10
## [241,] 60 30 10
## [242,] 60 31 10
## [243,] 59 31 10
## [244,] 59 31 10
## [245,] 58 32 10
## [246,] 49 37 15
## [247,] 57 33 10
## [248,] 57 33 10
## [249,] 57 33 10
## [250,] 56 34 10
## [251,] 56 34 10
## [252,] 56 34 10
## [253,] 55 34 10
## [254,] 54 35 11
## [255,] 53 36 11
## [256,] 54 36 11
## [257,] 54 36 11
## [258,] 53 36 11
## [259,] 52 37 11
## [260,] 52 37 11
## [261,] 52 37 11
## [262,] 50 38 12
## [263,] 51 38 11
## [264,] 51 38 11
## [265,] 50 39 11
## [266,] 50 39 11
## [267,] 50 39 11
## [268,] 49 40 11
## [269,] 49 40 11
## [270,] 48 40 11
## [271,] 48 41 11
## [272,] 47 41 11
## [273,] 47 42 11
## [274,] 47 42 11
## [275,] 46 42 11
## [276,] 46 43 12
## [277,] 46 43 11
## [278,] 45 43 11
## [279,] 45 44 11
## [280,] 44 44 11
## [281,] 44 44 12
## [282,] 42 43 15
## [283,] 43 44 14
## [284,] 43 45 12
## [285,] 42 45 13
## [286,] 42 46 12
## [287,] 42 46 12
## [288,] 41 47 12
## [289,] 41 46 13
## [290,] 41 47 12
## [291,] 40 48 12
## [292,] 40 48 12
## [293,] 39 49 12
## [294,] 39 49 12
## [295,] 39 50 12
## [296,] 38 50 12
## [297,] 38 50 12
## [298,] 38 51 12
## [299,] 37 51 12
## [300,] 38 50 13
## [301,] 37 51 12
## [302,] 36 52 12
## [303,] 36 52 12
## [304,] 35 53 12
## [305,] 35 53 12
## [306,] 35 53 12
## [307,] 34 54 12
## [308,] 34 53 12
## [309,] 34 55 12
## [310,] 33 55 12
## [311,] 33 55 12
## [312,] 33 55 12
## [313,] 32 56 12
## [314,] 32 56 12
## [315,] 32 57 12
## [316,] 32 56 12
## [317,] 31 57 12
## [318,] 31 58 12
## [319,] 30 58 12
## [320,] 30 58 12
## [321,] 32 55 13
## [322,] 29 59 12
## [323,] 31 56 13
## [324,] 29 59 12
## [325,] 29 59 12
## [326,] 28 60 12
## [327,] 28 60 12
## [328,] 28 61 12
## [329,] 30 56 14
## [330,] 27 62 12
## [331,] 27 62 12
## [332,] 30 56 14
## [333,] 26 62 12
## [334,] 25 63 11
## [335,] 25 64 11
## [336,] 25 63 12
## [337,] 25 63 12
## [338,] 26 62 12
## [339,] 24 64 11
## [340,] 24 64 11
## [341,] 23 66 11
## [342,] 24 65 11
## [343,] 23 66 11
## [344,] 23 65 12
## [345,] 24 64 12
## [346,] 22 67 11
## [347,] 22 66 11
## [348,] 23 65 12
## [349,] 22 67 11
## [350,] 21 69 11
## [351,] 28 56 16
## [352,] 21 68 11
## [353,] 20 69 11
## [354,] 20 70 11
## [355,] 20 69 11
## [356,] 19 70 11
## [357,] 24 63 13
## [358,] 19 71 11
## [359,] 19 70 11
## [360,] 19 70 11
## [361,] 29 51 19
## [362,] 19 71 11
## [363,] 18 71 11
## [364,] 18 72 10
## [365,] 19 69 11
## [366,] 18 71 11
## [367,] 18 71 11
## [368,] 24 61 15
## [369,] 23 62 15
## [370,] 17 73 10
## [371,] 17 73 10
## [372,] 16 74 10
## [373,] 18 71 11
## [374,] 16 73 10
## [375,] 17 73 11
## [376,] 15 74 10
## [377,] 15 75 10
## [378,] 15 76 10
## [379,] 15 75 10
## [380,] 15 74 11
## [381,] 14 76 10
## [382,] 15 75 10
## [383,] 15 74 11
## [384,] 14 75 10
## [385,] 23 59 17
## [386,] 13 77 9
## [387,] 16 73 12
## [388,] 14 77 10
## [389,] 13 78 9
## [390,] 13 77 10
## [391,] 13 77 10
## [392,] 13 77 10
## [393,] 12 78 9
## [394,] 13 77 10
## [395,] 13 78 10
## [396,] 12 79 9
## [397,] 12 78 10
## [398,] 21 63 17
## [399,] 11 80 9
## [400,] 12 79 9
## [401,] 12 78 10
## [402,] 12 78 10
## [403,] 12 78 10
## [404,] 12 78 10
## [405,] 11 80 9
## [406,] 11 79 10
## [407,] 11 80 9
## [408,] 11 79 10
## [409,] 15 71 13
## [410,] 11 80 9
## [411,] 10 81 9
## [412,] 11 79 10
## [413,] 10 81 9
## [414,] 11 79 10
## [415,] 10 80 9
## [416,] 10 81 9
## [417,] 11 78 11
## [418,] 10 80 10
## [419,] 10 80 10
## [420,] 10 80 10
## [421,] 10 80 10
## [422,] 11 79 10
## [423,] 10 80 10
## [424,] 9 81 9
## [425,] 12 76 12
## [426,] 10 80 10
## [427,] 10 81 10
## [428,] 10 79 10
## [429,] 9 81 9
## [430,] 10 79 10
## [431,] 10 80 10
## [432,] 9 81 10
## [433,] 10 79 11
## [434,] 10 79 11
## [435,] 11 77 12
## [436,] 9 80 10
## [437,] 9 80 10
## [438,] 10 80 11
## [439,] 12 75 13
## [440,] 9 80 11
## [441,] 9 80 10
## [442,] 9 81 10
## [443,] 9 80 11
## [444,] 25 47 28
## [445,] 10 79 11
## [446,] 9 80 11
## [447,] 9 80 11
## [448,] 9 79 11
## [449,] 9 79 11
## [450,] 9 80 11
## [451,] 13 72 15
## [452,] 9 79 11
## [453,] 10 78 12
## [454,] 10 78 12
## [455,] 20 56 24
## [456,] 9 79 12
## [457,] 11 75 14
## [458,] 9 78 12
## [459,] 10 77 13
## [460,] 10 77 13
## [461,] 10 78 13
## [462,] 20 55 25
## [463,] 11 75 14
## [464,] 10 77 13
## [465,] 10 77 13
## [466,] 10 75 14
## [467,] 12 72 16
## [468,] 10 76 14
## [469,] 10 75 15
## [470,] 10 75 15
## [471,] 13 69 18
## [472,] 10 76 14
## [473,] 10 75 15
## [474,] 13 68 19
## [475,] 11 73 16
## [476,] 10 74 16
## [477,] 10 74 16
## [478,] 10 74 16
## [479,] 10 74 16
## [480,] 11 72 17
## [481,] 10 73 16
## [482,] 10 74 16
## [483,] 12 70 19
## [484,] 11 71 18
## [485,] 10 72 17
## [486,] 10 73 17
## [487,] 10 72 17
## [488,] 13 65 22
## [489,] 11 70 19
## [490,] 11 71 19
## [491,] 11 70 19
## [492,] 13 66 22
## [493,] 11 71 19
## [494,] 11 69 20
## [495,] 11 70 19
## [496,] 11 69 20
## [497,] 11 68 20
## [498,] 13 65 23
## [499,] 11 69 20
## [500,] 11 67 21
## [501,] 11 69 21
## [502,] 11 68 21
## [503,] 14 61 26
## [504,] 14 60 26
## [505,] 11 67 22
## [506,] 11 67 22
## [507,] 11 66 22
## [508,] 11 66 23
## [509,] 12 64 24
## [510,] 11 65 23
## [511,] 11 65 24
## [512,] 11 65 24
## [513,] 11 65 24
## [514,] 15 56 29
## [515,] 12 64 25
## [516,] 11 64 25
## [517,] 12 63 26
## [518,] 11 63 25
## [519,] 12 62 26
## [520,] 12 62 27
## [521,] 12 61 27
## [522,] 12 62 27
## [523,] 12 61 27
## [524,] 12 60 28
## [525,] 12 61 28
## [526,] 12 60 28
## [527,] 12 60 28
## [528,] 12 60 29
## [529,] 12 58 29
## [530,] 12 58 30
## [531,] 12 58 30
## [532,] 12 58 30
## [533,] 27 38 35
## [534,] 13 55 32
## [535,] 12 57 31
## [536,] 12 57 31
## [537,] 12 56 32
## [538,] 12 56 32
## [539,] 12 56 32
## [540,] 12 56 33
## [541,] 14 51 35
## [542,] 12 55 33
## [543,] 12 54 34
## [544,] 12 54 34
## [545,] 12 54 34
## [546,] 13 51 36
## [547,] 12 53 35
## [548,] 12 53 35
## [549,] 13 50 37
## [550,] 12 52 36
## [551,] 12 51 37
## [552,] 21 41 38
## [553,] 12 51 37
## [554,] 16 45 39
## [555,] 12 50 38
## [556,] 12 49 38
## [557,] 20 41 38
## [558,] 12 49 39
## [559,] 12 49 39
## [560,] 12 48 40
## [561,] 12 48 40
## [562,] 12 47 41
## [563,] 12 47 41
## [564,] 12 47 41
## [565,] 12 46 42
## [566,] 12 46 42
## [567,] 12 45 42
## [568,] 12 45 43
## [569,] 12 45 43
## [570,] 12 45 44
## [571,] 12 44 44
## [572,] 18 41 41
## [573,] 12 44 45
## [574,] 12 43 45
## [575,] 11 43 46
## [576,] 12 43 46
## [577,] 12 42 46
## [578,] 11 42 47
## [579,] 11 42 47
## [580,] 11 41 47
## [581,] 12 41 48
## [582,] 11 40 48
## [583,] 11 40 49
## [584,] 11 40 49
## [585,] 11 39 50
## [586,] 11 39 49
## [587,] 11 39 50
## [588,] 11 38 51
## [589,] 11 38 51
## [590,] 11 38 51
## [591,] 11 37 52
## [592,] 12 38 51
## [593,] 11 37 53
## [594,] 11 36 53
## [595,] 11 36 53
## [596,] 11 36 54
## [597,] 11 35 54
## [598,] 11 35 54
## [599,] 11 35 54
## [600,] 10 34 55
## [601,] 10 34 56
## [602,] 10 33 56
## [603,] 11 34 56
## [604,] 11 34 56
## [605,] 10 33 57
## [606,] 10 32 57
## [607,] 10 32 58
## [608,] 10 31 59
## [609,] 10 31 59
## [610,] 10 31 60
## [611,] 10 30 60
## [612,] 10 30 60
## [613,] 10 30 60
## [614,] 10 29 61
## [615,] 10 29 61
## [616,] 9 29 62
## [617,] 9 29 62
## [618,] 9 28 62
## [619,] 9 28 63
## [620,] 9 27 63
## [621,] 10 29 61
## [622,] 9 27 64
## [623,] 9 27 64
## [624,] 9 27 64
## [625,] 9 27 64
## [626,] 9 26 65
## [627,] 9 26 65
## [628,] 9 26 65
## [629,] 9 26 65
## [630,] 12 31 57
## [631,] 9 25 66
## [632,] 8 24 68
## [633,] 9 25 66
## [634,] 8 24 68
## [635,] 10 28 62
## [636,] 8 23 68
## [637,] 8 23 69
## [638,] 9 24 67
## [639,] 8 22 70
## [640,] 9 24 67
## [641,] 8 22 70
## [642,] 8 22 70
## [643,] 15 32 53
## [644,] 8 21 72
## [645,] 8 22 69
## [646,] 8 21 72
## [647,] 11 28 60
## [648,] 7 20 72
## [649,] 7 20 73
## [650,] 7 19 74
## [651,] 8 20 72
## [652,] 7 19 74
## [653,] 10 25 65
## [654,] 8 20 72
## [655,] 7 19 74
## [656,] 7 18 75
## [657,] 7 18 76
## [658,] 7 19 74
## [659,] 7 19 74
## [660,] 7 17 76
## [661,] 7 17 77
## [662,] 7 17 77
## [663,] 7 18 74
## [664,] 9 23 67
## [665,] 6 16 78
## [666,] 6 16 78
## [667,] 7 18 75
## [668,] 6 16 78
## [669,] 6 16 78
## [670,] 6 15 80
## [671,] 6 16 78
## [672,] 6 15 79
## [673,] 16 32 52
## [674,] 6 15 79
## [675,] 6 14 80
## [676,] 9 22 69
## [677,] 5 13 81
## [678,] 6 14 81
## [679,] 5 13 81
## [680,] 5 13 82
## [681,] 5 13 82
## [682,] 6 14 81
## [683,] 5 13 82
## [684,] 8 18 75
## [685,] 5 13 81
## [686,] 5 12 83
## [687,] 6 13 81
## [688,] 6 14 81
## [689,] 5 12 83
## [690,] 5 12 83
## [691,] 6 13 81
## [692,] 5 11 84
## [693,] 5 11 84
## [694,] 5 13 82
## [695,] 5 12 83
## [696,] 5 11 84
## [697,] 5 12 82
## [698,] 5 11 84
## [699,] 5 11 84
## [700,] 5 12 83
## [701,] 5 11 84
## [702,] 4 10 85
## [703,] 5 10 85
## [704,] 5 11 84
## [705,] 5 12 83
## [706,] 5 12 83
## [707,] 5 11 84
## [708,] 5 11 84
## [709,] 5 12 82
## [710,] 5 11 84
## [711,] 7 15 78
## [712,] 4 10 86
## [713,] 5 11 83
## [714,] 9 18 73
## [715,] 4 10 86
## [716,] 5 11 84
## [717,] 4 9 87
## [718,] 9 20 70
## [719,] 4 10 86
## [720,] 6 13 81
## [721,] 4 10 86
## [722,] 4 9 87
## [723,] 4 10 86
## [724,] 4 9 87
## [725,] 4 10 86
## [726,] 11 22 68
## [727,] 4 9 87
## [728,] 4 9 87
## [729,] 4 10 86
## [730,] 4 9 87
## [731,] 5 11 84
## [732,] 5 11 83
## [733,] 4 9 86
## [734,] 4 9 87
## [735,] 10 20 70
## [736,] 5 10 85
## [737,] 5 10 85
## [738,] 4 9 86
## [739,] 4 9 87
## [740,] 4 9 87
## [741,] 4 9 86
## [742,] 6 12 82
## [743,] 5 10 85
## [744,] 5 10 86
## [745,] 4 9 87
## [746,] 5 10 85
## [747,] 9 19 72
## [748,] 4 9 86
## [749,] 5 11 84
## [750,] 5 9 86
## [751,] 5 10 85
## [752,] 5 10 85
## [753,] 5 9 86
## [754,] 5 10 85
## [755,] 5 10 85
## [756,] 5 10 85
## [757,] 5 10 85
## [758,] 5 11 84
## [759,] 6 12 82
## [760,] 5 11 84
## [761,] 7 15 77
## [762,] 5 11 84
## [763,] 5 11 84
## [764,] 6 12 83
## [765,] 6 11 83
## [766,] 6 12 82
## [767,] 6 11 83
## [768,] 5 10 85
## [769,] 5 10 84
## [770,] 6 13 81
## [771,] 6 12 83
## [772,] 6 11 83
## [773,] 6 11 83
## [774,] 6 11 84
## [775,] 5 11 84
## [776,] 6 12 82
## [777,] 6 13 81
## [778,] 6 11 83
## [779,] 6 12 83
## [780,] 6 12 82
## [781,] 6 11 83
## [782,] 6 11 83
## [783,] 6 11 83
## [784,] 6 12 83
## [785,] 7 14 79
## [786,] 6 12 82
## [787,] 6 12 82
## [788,] 6 12 82
## [789,] 6 12 82
## [790,] 6 12 82
## [791,] 6 13 81
## [792,] 19 31 50
## [793,] 7 13 80
## [794,] 8 15 77
## [795,] 7 13 80
## [796,] 7 13 80
## [797,] 7 13 80
## [798,] 7 13 80
## [799,] 7 13 80
## [800,] 7 13 80
## [801,] 7 14 79
## [802,] 9 17 73
## [803,] 7 14 78
## [804,] 7 14 79
## [805,] 7 13 79
## [806,] 7 14 78
## [807,] 7 14 79
## [808,] 7 14 78
## [809,] 8 15 77
## [810,] 8 15 77
## [811,] 8 14 78
## [812,] 8 15 77
## [813,] 8 15 77
## [814,] 8 14 78
## [815,] 8 15 77
## [816,] 8 15 78
## [817,] 8 15 77
## [818,] 8 15 77
## [819,] 8 15 77
## [820,] 8 15 77
## [821,] 8 15 76
## [822,] 8 15 76
## [823,] 9 16 76
## [824,] 8 16 76
## [825,] 9 16 76
## [826,] 11 19 70
## [827,] 9 17 75
## [828,] 9 16 75
## [829,] 9 16 75
## [830,] 9 16 75
## [831,] 9 16 75
## [832,] 9 17 74
## [833,] 9 16 75
## [834,] 9 16 75
## [835,] 9 17 74
## [836,] 9 17 74
## [837,] 9 17 74
## [838,] 9 17 73
## [839,] 9 17 74
## [840,] 9 17 73
## [841,] 10 17 73
## [842,] 10 18 73
## [843,] 10 17 73
## [844,] 10 17 73
## [845,] 10 18 73
## [846,] 10 18 72
## [847,] 10 18 72
## [848,] 10 19 71
## [849,] 10 18 72
## [850,] 10 19 71
## Fuzzyness coefficients:
## dunn_coeff normalized
## 0.5755568 0.3633352
## Closest hard clustering:
## [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
## [36] 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
## [71] 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
## [106] 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
## [141] 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
## [176] 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
## [211] 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
## [246] 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
## [281] 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
## [316] 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
## [351] 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
## [386] 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
## [421] 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
## [456] 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
## [491] 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
## [526] 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
## [561] 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [596] 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 3 3 3 3 3
## [631] 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 3 3 3 3 3
## [666] 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 3 3 3 3 3
## [701] 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 3 3 3 3 3
## [736] 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 3 3 3 3 3
## [771] 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 3 3 3 3 3
## [806] 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 3 3 3 3 3
## [841] 3 3 3 3 3 3 3 3 3 3
##
## Available components:
## [1] "membership" "coeff" "memb.exp" "clustering" "k.crisp"
## [6] "objective" "convergence" "diss" "call" "silinfo"
## [11] "data"summary(cliente_agrupa)## Fuzzy Clustering object of class 'fanny' :
## m.ship.expon. 2
## objective 31231.79
## tolerance 1e-15
## iterations 51
## converged 1
## maxit 500
## n 850
## Membership coefficients (in %, rounded):
## [,1] [,2] [,3]
## [1,] 71 19 10
## [2,] 69 20 11
## [3,] 71 18 10
## [4,] 71 18 10
## [5,] 55 28 17
## [6,] 71 19 10
## [7,] 72 18 10
## [8,] 72 18 10
## [9,] 72 18 10
## [10,] 68 20 11
## [11,] 71 19 10
## [12,] 73 17 10
## [13,] 72 18 10
## [14,] 73 17 10
## [15,] 73 17 10
## [16,] 73 18 10
## [17,] 73 17 10
## [18,] 74 17 9
## [19,] 74 17 9
## [20,] 74 17 9
## [21,] 74 17 9
## [22,] 75 16 9
## [23,] 75 16 9
## [24,] 73 17 9
## [25,] 69 20 11
## [26,] 75 16 9
## [27,] 75 16 9
## [28,] 76 16 9
## [29,] 75 16 9
## [30,] 76 16 8
## [31,] 72 18 10
## [32,] 76 16 8
## [33,] 76 16 8
## [34,] 76 15 8
## [35,] 77 15 8
## [36,] 77 15 8
## [37,] 77 15 8
## [38,] 76 15 8
## [39,] 76 15 8
## [40,] 70 19 10
## [41,] 76 15 8
## [42,] 72 18 10
## [43,] 78 15 8
## [44,] 64 23 13
## [45,] 77 15 8
## [46,] 76 16 8
## [47,] 77 15 8
## [48,] 78 14 7
## [49,] 78 14 8
## [50,] 79 14 7
## [51,] 77 15 8
## [52,] 78 14 7
## [53,] 78 14 7
## [54,] 78 15 8
## [55,] 78 14 7
## [56,] 80 13 7
## [57,] 79 14 7
## [58,] 80 13 7
## [59,] 80 13 7
## [60,] 80 13 7
## [61,] 80 13 7
## [62,] 81 13 6
## [63,] 80 13 7
## [64,] 79 14 7
## [65,] 80 13 7
## [66,] 82 12 6
## [67,] 79 14 7
## [68,] 82 12 6
## [69,] 81 12 6
## [70,] 80 13 7
## [71,] 82 12 6
## [72,] 80 13 7
## [73,] 77 15 8
## [74,] 83 12 6
## [75,] 83 11 6
## [76,] 82 12 6
## [77,] 83 11 6
## [78,] 82 12 6
## [79,] 69 20 11
## [80,] 83 12 6
## [81,] 82 12 6
## [82,] 81 13 6
## [83,] 67 21 11
## [84,] 84 11 5
## [85,] 83 11 5
## [86,] 82 12 6
## [87,] 83 11 5
## [88,] 83 11 6
## [89,] 85 10 5
## [90,] 84 11 5
## [91,] 82 12 6
## [92,] 85 10 5
## [93,] 84 11 5
## [94,] 84 11 5
## [95,] 85 10 5
## [96,] 85 10 5
## [97,] 85 10 5
## [98,] 85 10 5
## [99,] 85 10 5
## [100,] 84 11 5
## [101,] 85 10 5
## [102,] 73 18 9
## [103,] 85 10 5
## [104,] 86 10 5
## [105,] 85 10 5
## [106,] 84 11 5
## [107,] 86 9 4
## [108,] 86 10 5
## [109,] 86 10 4
## [110,] 82 12 6
## [111,] 86 9 4
## [112,] 86 10 4
## [113,] 87 9 4
## [114,] 84 11 5
## [115,] 86 9 4
## [116,] 87 9 4
## [117,] 85 10 5
## [118,] 87 9 4
## [119,] 85 10 5
## [120,] 87 9 4
## [121,] 86 10 4
## [122,] 87 9 4
## [123,] 87 9 4
## [124,] 86 9 4
## [125,] 87 9 4
## [126,] 84 11 5
## [127,] 87 9 4
## [128,] 86 10 4
## [129,] 85 10 5
## [130,] 85 11 5
## [131,] 87 9 4
## [132,] 86 10 4
## [133,] 86 9 4
## [134,] 86 10 4
## [135,] 86 10 4
## [136,] 87 9 4
## [137,] 80 14 6
## [138,] 85 10 5
## [139,] 86 10 4
## [140,] 86 9 4
## [141,] 86 9 4
## [142,] 85 11 5
## [143,] 86 9 4
## [144,] 85 10 4
## [145,] 83 12 5
## [146,] 85 10 4
## [147,] 85 11 5
## [148,] 85 10 4
## [149,] 85 10 4
## [150,] 85 11 5
## [151,] 85 10 4
## [152,] 84 11 5
## [153,] 85 10 4
## [154,] 83 12 5
## [155,] 83 12 5
## [156,] 85 11 5
## [157,] 73 19 8
## [158,] 75 17 7
## [159,] 83 12 5
## [160,] 83 12 5
## [161,] 82 12 5
## [162,] 84 11 5
## [163,] 79 15 6
## [164,] 84 12 5
## [165,] 82 12 5
## [166,] 83 12 5
## [167,] 82 12 5
## [168,] 82 13 5
## [169,] 82 13 5
## [170,] 82 13 5
## [171,] 76 17 7
## [172,] 81 14 6
## [173,] 81 13 5
## [174,] 82 13 5
## [175,] 81 14 5
## [176,] 78 15 6
## [177,] 80 14 6
## [178,] 80 15 6
## [179,] 81 14 6
## [180,] 74 19 8
## [181,] 80 14 6
## [182,] 80 14 6
## [183,] 78 16 6
## [184,] 65 25 10
## [185,] 76 17 7
## [186,] 78 16 6
## [187,] 77 16 6
## [188,] 78 16 6
## [189,] 78 16 6
## [190,] 75 18 7
## [191,] 77 17 6
## [192,] 76 18 7
## [193,] 74 19 7
## [194,] 76 17 7
## [195,] 74 19 7
## [196,] 76 17 7
## [197,] 74 19 7
## [198,] 73 20 7
## [199,] 51 33 16
## [200,] 74 19 7
## [201,] 65 25 10
## [202,] 73 20 7
## [203,] 73 20 7
## [204,] 73 20 7
## [205,] 72 20 7
## [206,] 72 20 7
## [207,] 72 20 7
## [208,] 49 34 17
## [209,] 66 25 9
## [210,] 71 22 8
## [211,] 71 22 8
## [212,] 71 22 8
## [213,] 70 22 8
## [214,] 67 24 9
## [215,] 69 23 8
## [216,] 68 24 8
## [217,] 68 24 8
## [218,] 67 24 9
## [219,] 68 24 8
## [220,] 67 25 9
## [221,] 66 25 9
## [222,] 60 29 11
## [223,] 67 25 8
## [224,] 66 26 9
## [225,] 66 26 9
## [226,] 65 26 9
## [227,] 60 29 11
## [228,] 65 26 9
## [229,] 64 27 9
## [230,] 64 27 9
## [231,] 64 27 9
## [232,] 63 28 9
## [233,] 63 28 9
## [234,] 62 29 10
## [235,] 60 30 10
## [236,] 61 29 10
## [237,] 61 29 9
## [238,] 60 30 10
## [239,] 60 30 10
## [240,] 60 30 10
## [241,] 60 30 10
## [242,] 60 31 10
## [243,] 59 31 10
## [244,] 59 31 10
## [245,] 58 32 10
## [246,] 49 37 15
## [247,] 57 33 10
## [248,] 57 33 10
## [249,] 57 33 10
## [250,] 56 34 10
## [251,] 56 34 10
## [252,] 56 34 10
## [253,] 55 34 10
## [254,] 54 35 11
## [255,] 53 36 11
## [256,] 54 36 11
## [257,] 54 36 11
## [258,] 53 36 11
## [259,] 52 37 11
## [260,] 52 37 11
## [261,] 52 37 11
## [262,] 50 38 12
## [263,] 51 38 11
## [264,] 51 38 11
## [265,] 50 39 11
## [266,] 50 39 11
## [267,] 50 39 11
## [268,] 49 40 11
## [269,] 49 40 11
## [270,] 48 40 11
## [271,] 48 41 11
## [272,] 47 41 11
## [273,] 47 42 11
## [274,] 47 42 11
## [275,] 46 42 11
## [276,] 46 43 12
## [277,] 46 43 11
## [278,] 45 43 11
## [279,] 45 44 11
## [280,] 44 44 11
## [281,] 44 44 12
## [282,] 42 43 15
## [283,] 43 44 14
## [284,] 43 45 12
## [285,] 42 45 13
## [286,] 42 46 12
## [287,] 42 46 12
## [288,] 41 47 12
## [289,] 41 46 13
## [290,] 41 47 12
## [291,] 40 48 12
## [292,] 40 48 12
## [293,] 39 49 12
## [294,] 39 49 12
## [295,] 39 50 12
## [296,] 38 50 12
## [297,] 38 50 12
## [298,] 38 51 12
## [299,] 37 51 12
## [300,] 38 50 13
## [301,] 37 51 12
## [302,] 36 52 12
## [303,] 36 52 12
## [304,] 35 53 12
## [305,] 35 53 12
## [306,] 35 53 12
## [307,] 34 54 12
## [308,] 34 53 12
## [309,] 34 55 12
## [310,] 33 55 12
## [311,] 33 55 12
## [312,] 33 55 12
## [313,] 32 56 12
## [314,] 32 56 12
## [315,] 32 57 12
## [316,] 32 56 12
## [317,] 31 57 12
## [318,] 31 58 12
## [319,] 30 58 12
## [320,] 30 58 12
## [321,] 32 55 13
## [322,] 29 59 12
## [323,] 31 56 13
## [324,] 29 59 12
## [325,] 29 59 12
## [326,] 28 60 12
## [327,] 28 60 12
## [328,] 28 61 12
## [329,] 30 56 14
## [330,] 27 62 12
## [331,] 27 62 12
## [332,] 30 56 14
## [333,] 26 62 12
## [334,] 25 63 11
## [335,] 25 64 11
## [336,] 25 63 12
## [337,] 25 63 12
## [338,] 26 62 12
## [339,] 24 64 11
## [340,] 24 64 11
## [341,] 23 66 11
## [342,] 24 65 11
## [343,] 23 66 11
## [344,] 23 65 12
## [345,] 24 64 12
## [346,] 22 67 11
## [347,] 22 66 11
## [348,] 23 65 12
## [349,] 22 67 11
## [350,] 21 69 11
## [351,] 28 56 16
## [352,] 21 68 11
## [353,] 20 69 11
## [354,] 20 70 11
## [355,] 20 69 11
## [356,] 19 70 11
## [357,] 24 63 13
## [358,] 19 71 11
## [359,] 19 70 11
## [360,] 19 70 11
## [361,] 29 51 19
## [362,] 19 71 11
## [363,] 18 71 11
## [364,] 18 72 10
## [365,] 19 69 11
## [366,] 18 71 11
## [367,] 18 71 11
## [368,] 24 61 15
## [369,] 23 62 15
## [370,] 17 73 10
## [371,] 17 73 10
## [372,] 16 74 10
## [373,] 18 71 11
## [374,] 16 73 10
## [375,] 17 73 11
## [376,] 15 74 10
## [377,] 15 75 10
## [378,] 15 76 10
## [379,] 15 75 10
## [380,] 15 74 11
## [381,] 14 76 10
## [382,] 15 75 10
## [383,] 15 74 11
## [384,] 14 75 10
## [385,] 23 59 17
## [386,] 13 77 9
## [387,] 16 73 12
## [388,] 14 77 10
## [389,] 13 78 9
## [390,] 13 77 10
## [391,] 13 77 10
## [392,] 13 77 10
## [393,] 12 78 9
## [394,] 13 77 10
## [395,] 13 78 10
## [396,] 12 79 9
## [397,] 12 78 10
## [398,] 21 63 17
## [399,] 11 80 9
## [400,] 12 79 9
## [401,] 12 78 10
## [402,] 12 78 10
## [403,] 12 78 10
## [404,] 12 78 10
## [405,] 11 80 9
## [406,] 11 79 10
## [407,] 11 80 9
## [408,] 11 79 10
## [409,] 15 71 13
## [410,] 11 80 9
## [411,] 10 81 9
## [412,] 11 79 10
## [413,] 10 81 9
## [414,] 11 79 10
## [415,] 10 80 9
## [416,] 10 81 9
## [417,] 11 78 11
## [418,] 10 80 10
## [419,] 10 80 10
## [420,] 10 80 10
## [421,] 10 80 10
## [422,] 11 79 10
## [423,] 10 80 10
## [424,] 9 81 9
## [425,] 12 76 12
## [426,] 10 80 10
## [427,] 10 81 10
## [428,] 10 79 10
## [429,] 9 81 9
## [430,] 10 79 10
## [431,] 10 80 10
## [432,] 9 81 10
## [433,] 10 79 11
## [434,] 10 79 11
## [435,] 11 77 12
## [436,] 9 80 10
## [437,] 9 80 10
## [438,] 10 80 11
## [439,] 12 75 13
## [440,] 9 80 11
## [441,] 9 80 10
## [442,] 9 81 10
## [443,] 9 80 11
## [444,] 25 47 28
## [445,] 10 79 11
## [446,] 9 80 11
## [447,] 9 80 11
## [448,] 9 79 11
## [449,] 9 79 11
## [450,] 9 80 11
## [451,] 13 72 15
## [452,] 9 79 11
## [453,] 10 78 12
## [454,] 10 78 12
## [455,] 20 56 24
## [456,] 9 79 12
## [457,] 11 75 14
## [458,] 9 78 12
## [459,] 10 77 13
## [460,] 10 77 13
## [461,] 10 78 13
## [462,] 20 55 25
## [463,] 11 75 14
## [464,] 10 77 13
## [465,] 10 77 13
## [466,] 10 75 14
## [467,] 12 72 16
## [468,] 10 76 14
## [469,] 10 75 15
## [470,] 10 75 15
## [471,] 13 69 18
## [472,] 10 76 14
## [473,] 10 75 15
## [474,] 13 68 19
## [475,] 11 73 16
## [476,] 10 74 16
## [477,] 10 74 16
## [478,] 10 74 16
## [479,] 10 74 16
## [480,] 11 72 17
## [481,] 10 73 16
## [482,] 10 74 16
## [483,] 12 70 19
## [484,] 11 71 18
## [485,] 10 72 17
## [486,] 10 73 17
## [487,] 10 72 17
## [488,] 13 65 22
## [489,] 11 70 19
## [490,] 11 71 19
## [491,] 11 70 19
## [492,] 13 66 22
## [493,] 11 71 19
## [494,] 11 69 20
## [495,] 11 70 19
## [496,] 11 69 20
## [497,] 11 68 20
## [498,] 13 65 23
## [499,] 11 69 20
## [500,] 11 67 21
## [501,] 11 69 21
## [502,] 11 68 21
## [503,] 14 61 26
## [504,] 14 60 26
## [505,] 11 67 22
## [506,] 11 67 22
## [507,] 11 66 22
## [508,] 11 66 23
## [509,] 12 64 24
## [510,] 11 65 23
## [511,] 11 65 24
## [512,] 11 65 24
## [513,] 11 65 24
## [514,] 15 56 29
## [515,] 12 64 25
## [516,] 11 64 25
## [517,] 12 63 26
## [518,] 11 63 25
## [519,] 12 62 26
## [520,] 12 62 27
## [521,] 12 61 27
## [522,] 12 62 27
## [523,] 12 61 27
## [524,] 12 60 28
## [525,] 12 61 28
## [526,] 12 60 28
## [527,] 12 60 28
## [528,] 12 60 29
## [529,] 12 58 29
## [530,] 12 58 30
## [531,] 12 58 30
## [532,] 12 58 30
## [533,] 27 38 35
## [534,] 13 55 32
## [535,] 12 57 31
## [536,] 12 57 31
## [537,] 12 56 32
## [538,] 12 56 32
## [539,] 12 56 32
## [540,] 12 56 33
## [541,] 14 51 35
## [542,] 12 55 33
## [543,] 12 54 34
## [544,] 12 54 34
## [545,] 12 54 34
## [546,] 13 51 36
## [547,] 12 53 35
## [548,] 12 53 35
## [549,] 13 50 37
## [550,] 12 52 36
## [551,] 12 51 37
## [552,] 21 41 38
## [553,] 12 51 37
## [554,] 16 45 39
## [555,] 12 50 38
## [556,] 12 49 38
## [557,] 20 41 38
## [558,] 12 49 39
## [559,] 12 49 39
## [560,] 12 48 40
## [561,] 12 48 40
## [562,] 12 47 41
## [563,] 12 47 41
## [564,] 12 47 41
## [565,] 12 46 42
## [566,] 12 46 42
## [567,] 12 45 42
## [568,] 12 45 43
## [569,] 12 45 43
## [570,] 12 45 44
## [571,] 12 44 44
## [572,] 18 41 41
## [573,] 12 44 45
## [574,] 12 43 45
## [575,] 11 43 46
## [576,] 12 43 46
## [577,] 12 42 46
## [578,] 11 42 47
## [579,] 11 42 47
## [580,] 11 41 47
## [581,] 12 41 48
## [582,] 11 40 48
## [583,] 11 40 49
## [584,] 11 40 49
## [585,] 11 39 50
## [586,] 11 39 49
## [587,] 11 39 50
## [588,] 11 38 51
## [589,] 11 38 51
## [590,] 11 38 51
## [591,] 11 37 52
## [592,] 12 38 51
## [593,] 11 37 53
## [594,] 11 36 53
## [595,] 11 36 53
## [596,] 11 36 54
## [597,] 11 35 54
## [598,] 11 35 54
## [599,] 11 35 54
## [600,] 10 34 55
## [601,] 10 34 56
## [602,] 10 33 56
## [603,] 11 34 56
## [604,] 11 34 56
## [605,] 10 33 57
## [606,] 10 32 57
## [607,] 10 32 58
## [608,] 10 31 59
## [609,] 10 31 59
## [610,] 10 31 60
## [611,] 10 30 60
## [612,] 10 30 60
## [613,] 10 30 60
## [614,] 10 29 61
## [615,] 10 29 61
## [616,] 9 29 62
## [617,] 9 29 62
## [618,] 9 28 62
## [619,] 9 28 63
## [620,] 9 27 63
## [621,] 10 29 61
## [622,] 9 27 64
## [623,] 9 27 64
## [624,] 9 27 64
## [625,] 9 27 64
## [626,] 9 26 65
## [627,] 9 26 65
## [628,] 9 26 65
## [629,] 9 26 65
## [630,] 12 31 57
## [631,] 9 25 66
## [632,] 8 24 68
## [633,] 9 25 66
## [634,] 8 24 68
## [635,] 10 28 62
## [636,] 8 23 68
## [637,] 8 23 69
## [638,] 9 24 67
## [639,] 8 22 70
## [640,] 9 24 67
## [641,] 8 22 70
## [642,] 8 22 70
## [643,] 15 32 53
## [644,] 8 21 72
## [645,] 8 22 69
## [646,] 8 21 72
## [647,] 11 28 60
## [648,] 7 20 72
## [649,] 7 20 73
## [650,] 7 19 74
## [651,] 8 20 72
## [652,] 7 19 74
## [653,] 10 25 65
## [654,] 8 20 72
## [655,] 7 19 74
## [656,] 7 18 75
## [657,] 7 18 76
## [658,] 7 19 74
## [659,] 7 19 74
## [660,] 7 17 76
## [661,] 7 17 77
## [662,] 7 17 77
## [663,] 7 18 74
## [664,] 9 23 67
## [665,] 6 16 78
## [666,] 6 16 78
## [667,] 7 18 75
## [668,] 6 16 78
## [669,] 6 16 78
## [670,] 6 15 80
## [671,] 6 16 78
## [672,] 6 15 79
## [673,] 16 32 52
## [674,] 6 15 79
## [675,] 6 14 80
## [676,] 9 22 69
## [677,] 5 13 81
## [678,] 6 14 81
## [679,] 5 13 81
## [680,] 5 13 82
## [681,] 5 13 82
## [682,] 6 14 81
## [683,] 5 13 82
## [684,] 8 18 75
## [685,] 5 13 81
## [686,] 5 12 83
## [687,] 6 13 81
## [688,] 6 14 81
## [689,] 5 12 83
## [690,] 5 12 83
## [691,] 6 13 81
## [692,] 5 11 84
## [693,] 5 11 84
## [694,] 5 13 82
## [695,] 5 12 83
## [696,] 5 11 84
## [697,] 5 12 82
## [698,] 5 11 84
## [699,] 5 11 84
## [700,] 5 12 83
## [701,] 5 11 84
## [702,] 4 10 85
## [703,] 5 10 85
## [704,] 5 11 84
## [705,] 5 12 83
## [706,] 5 12 83
## [707,] 5 11 84
## [708,] 5 11 84
## [709,] 5 12 82
## [710,] 5 11 84
## [711,] 7 15 78
## [712,] 4 10 86
## [713,] 5 11 83
## [714,] 9 18 73
## [715,] 4 10 86
## [716,] 5 11 84
## [717,] 4 9 87
## [718,] 9 20 70
## [719,] 4 10 86
## [720,] 6 13 81
## [721,] 4 10 86
## [722,] 4 9 87
## [723,] 4 10 86
## [724,] 4 9 87
## [725,] 4 10 86
## [726,] 11 22 68
## [727,] 4 9 87
## [728,] 4 9 87
## [729,] 4 10 86
## [730,] 4 9 87
## [731,] 5 11 84
## [732,] 5 11 83
## [733,] 4 9 86
## [734,] 4 9 87
## [735,] 10 20 70
## [736,] 5 10 85
## [737,] 5 10 85
## [738,] 4 9 86
## [739,] 4 9 87
## [740,] 4 9 87
## [741,] 4 9 86
## [742,] 6 12 82
## [743,] 5 10 85
## [744,] 5 10 86
## [745,] 4 9 87
## [746,] 5 10 85
## [747,] 9 19 72
## [748,] 4 9 86
## [749,] 5 11 84
## [750,] 5 9 86
## [751,] 5 10 85
## [752,] 5 10 85
## [753,] 5 9 86
## [754,] 5 10 85
## [755,] 5 10 85
## [756,] 5 10 85
## [757,] 5 10 85
## [758,] 5 11 84
## [759,] 6 12 82
## [760,] 5 11 84
## [761,] 7 15 77
## [762,] 5 11 84
## [763,] 5 11 84
## [764,] 6 12 83
## [765,] 6 11 83
## [766,] 6 12 82
## [767,] 6 11 83
## [768,] 5 10 85
## [769,] 5 10 84
## [770,] 6 13 81
## [771,] 6 12 83
## [772,] 6 11 83
## [773,] 6 11 83
## [774,] 6 11 84
## [775,] 5 11 84
## [776,] 6 12 82
## [777,] 6 13 81
## [778,] 6 11 83
## [779,] 6 12 83
## [780,] 6 12 82
## [781,] 6 11 83
## [782,] 6 11 83
## [783,] 6 11 83
## [784,] 6 12 83
## [785,] 7 14 79
## [786,] 6 12 82
## [787,] 6 12 82
## [788,] 6 12 82
## [789,] 6 12 82
## [790,] 6 12 82
## [791,] 6 13 81
## [792,] 19 31 50
## [793,] 7 13 80
## [794,] 8 15 77
## [795,] 7 13 80
## [796,] 7 13 80
## [797,] 7 13 80
## [798,] 7 13 80
## [799,] 7 13 80
## [800,] 7 13 80
## [801,] 7 14 79
## [802,] 9 17 73
## [803,] 7 14 78
## [804,] 7 14 79
## [805,] 7 13 79
## [806,] 7 14 78
## [807,] 7 14 79
## [808,] 7 14 78
## [809,] 8 15 77
## [810,] 8 15 77
## [811,] 8 14 78
## [812,] 8 15 77
## [813,] 8 15 77
## [814,] 8 14 78
## [815,] 8 15 77
## [816,] 8 15 78
## [817,] 8 15 77
## [818,] 8 15 77
## [819,] 8 15 77
## [820,] 8 15 77
## [821,] 8 15 76
## [822,] 8 15 76
## [823,] 9 16 76
## [824,] 8 16 76
## [825,] 9 16 76
## [826,] 11 19 70
## [827,] 9 17 75
## [828,] 9 16 75
## [829,] 9 16 75
## [830,] 9 16 75
## [831,] 9 16 75
## [832,] 9 17 74
## [833,] 9 16 75
## [834,] 9 16 75
## [835,] 9 17 74
## [836,] 9 17 74
## [837,] 9 17 74
## [838,] 9 17 73
## [839,] 9 17 74
## [840,] 9 17 73
## [841,] 10 17 73
## [842,] 10 18 73
## [843,] 10 17 73
## [844,] 10 17 73
## [845,] 10 18 73
## [846,] 10 18 72
## [847,] 10 18 72
## [848,] 10 19 71
## [849,] 10 18 72
## [850,] 10 19 71
## Fuzzyness coefficients:
## dunn_coeff normalized
## 0.5755568 0.3633352
## Closest hard clustering:
## [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
## [36] 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
## [71] 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
## [106] 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
## [141] 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
## [176] 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
## [211] 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
## [246] 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
## [281] 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
## [316] 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
## [351] 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
## [386] 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
## [421] 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
## [456] 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
## [491] 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
## [526] 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
## [561] 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [596] 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 3 3 3 3 3
## [631] 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 3 3 3 3 3
## [666] 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 3 3 3 3 3
## [701] 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 3 3 3 3 3
## [736] 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 3 3 3 3 3
## [771] 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 3 3 3 3 3
## [806] 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 3 3 3 3 3
## [841] 3 3 3 3 3 3 3 3 3 3
##
## Silhouette plot information:
## cluster neighbor sil_width
## 113 1 2 0.7405589415
## 104 1 2 0.7380669749
## 118 1 2 0.7377222856
## 116 1 2 0.7376134780
## 98 1 2 0.7374440176
## 111 1 2 0.7372321697
## 107 1 2 0.7372051299
## 122 1 2 0.7371321446
## 125 1 2 0.7371243895
## 109 1 2 0.7369524317
## 92 1 2 0.7367982048
## 99 1 2 0.7366721595
## 97 1 2 0.7361649071
## 123 1 2 0.7357682158
## 89 1 2 0.7356583469
## 84 1 2 0.7352002937
## 108 1 2 0.7349528091
## 95 1 2 0.7346904066
## 112 1 2 0.7342344804
## 96 1 2 0.7338820309
## 120 1 2 0.7338467818
## 115 1 2 0.7334051206
## 101 1 2 0.7331718230
## 127 1 2 0.7326319792
## 105 1 2 0.7322412444
## 124 1 2 0.7315512126
## 103 1 2 0.7314368410
## 90 1 2 0.7313002102
## 77 1 2 0.7304881776
## 131 1 2 0.7302577492
## 85 1 2 0.7299071837
## 75 1 2 0.7294682962
## 121 1 2 0.7292104573
## 93 1 2 0.7290286736
## 74 1 2 0.7287946865
## 87 1 2 0.7281477931
## 94 1 2 0.7276948614
## 117 1 2 0.7276035288
## 128 1 2 0.7273344487
## 119 1 2 0.7268657353
## 80 1 2 0.7268503651
## 100 1 2 0.7267149895
## 136 1 2 0.7262695872
## 133 1 2 0.7261703480
## 66 1 2 0.7259027265
## 132 1 2 0.7257735602
## 78 1 2 0.7247014736
## 88 1 2 0.7244816704
## 135 1 2 0.7239563097
## 141 1 2 0.7237291804
## 81 1 2 0.7237002626
## 140 1 2 0.7233323633
## 134 1 2 0.7229375018
## 106 1 2 0.7227952180
## 71 1 2 0.7226758414
## 114 1 2 0.7224598516
## 68 1 2 0.7221558952
## 76 1 2 0.7219216030
## 139 1 2 0.7218076669
## 143 1 2 0.7217377831
## 86 1 2 0.7210708122
## 129 1 2 0.7207159152
## 69 1 2 0.7206346575
## 91 1 2 0.7195549607
## 62 1 2 0.7193395158
## 61 1 2 0.7160448246
## 60 1 2 0.7153680531
## 130 1 2 0.7152862869
## 82 1 2 0.7145634846
## 65 1 2 0.7143711830
## 144 1 2 0.7143709450
## 138 1 2 0.7140158534
## 58 1 2 0.7140122619
## 126 1 2 0.7139740959
## 59 1 2 0.7137239063
## 63 1 2 0.7135485804
## 56 1 2 0.7128699119
## 148 1 2 0.7128535221
## 146 1 2 0.7126556462
## 70 1 2 0.7124508023
## 72 1 2 0.7121202997
## 149 1 2 0.7119544913
## 151 1 2 0.7113435441
## 142 1 2 0.7103096299
## 147 1 2 0.7100179079
## 57 1 2 0.7098611076
## 110 1 2 0.7095157618
## 153 1 2 0.7083288584
## 50 1 2 0.7076869162
## 64 1 2 0.7073895132
## 150 1 2 0.7070148945
## 67 1 2 0.7062594623
## 55 1 2 0.7036108401
## 156 1 2 0.7035057403
## 52 1 2 0.7034895115
## 48 1 2 0.7030106117
## 152 1 2 0.7028869036
## 49 1 2 0.7022617098
## 53 1 2 0.7015892166
## 43 1 2 0.7010372787
## 54 1 2 0.6996993959
## 37 1 2 0.6992478281
## 45 1 2 0.6986061798
## 36 1 2 0.6985657480
## 145 1 2 0.6978137878
## 35 1 2 0.6970456692
## 155 1 2 0.6953442918
## 47 1 2 0.6947603325
## 162 1 2 0.6943234628
## 154 1 2 0.6938754829
## 34 1 2 0.6931392010
## 164 1 2 0.6928931599
## 30 1 2 0.6925606530
## 160 1 2 0.6921763339
## 33 1 2 0.6921439899
## 159 1 2 0.6917511109
## 32 1 2 0.6914785200
## 38 1 2 0.6912376183
## 51 1 2 0.6904365183
## 39 1 2 0.6902822372
## 41 1 2 0.6894625827
## 28 1 2 0.6876433357
## 73 1 2 0.6872854459
## 29 1 2 0.6870607369
## 166 1 2 0.6864077809
## 46 1 2 0.6861715045
## 161 1 2 0.6834418900
## 22 1 2 0.6828578936
## 137 1 2 0.6827831178
## 167 1 2 0.6825583215
## 165 1 2 0.6823936177
## 23 1 2 0.6816804025
## 26 1 2 0.6810609467
## 27 1 2 0.6803558533
## 168 1 2 0.6789552373
## 20 1 2 0.6780612423
## 21 1 2 0.6770002261
## 170 1 2 0.6760806619
## 169 1 2 0.6743091433
## 19 1 2 0.6738337131
## 18 1 2 0.6727017665
## 174 1 2 0.6713028666
## 24 1 2 0.6707783957
## 173 1 2 0.6706929514
## 12 1 2 0.6694310301
## 14 1 2 0.6682675932
## 15 1 2 0.6673495854
## 17 1 2 0.6672508908
## 172 1 2 0.6671924527
## 175 1 2 0.6657795852
## 16 1 2 0.6652951580
## 13 1 2 0.6632876715
## 7 1 2 0.6614192762
## 179 1 2 0.6609023263
## 177 1 2 0.6602336809
## 8 1 2 0.6598450818
## 163 1 2 0.6586550732
## 9 1 2 0.6584684178
## 31 1 2 0.6580372861
## 3 1 2 0.6554722174
## 182 1 2 0.6544868491
## 181 1 2 0.6540027923
## 178 1 2 0.6537628385
## 4 1 2 0.6533803553
## 11 1 2 0.6501286916
## 42 1 2 0.6500035067
## 1 1 2 0.6493617669
## 6 1 2 0.6479891917
## 176 1 2 0.6436248338
## 102 1 2 0.6424448232
## 183 1 2 0.6394754765
## 40 1 2 0.6338727223
## 186 1 2 0.6334699052
## 158 1 2 0.6325411011
## 171 1 2 0.6318683760
## 189 1 2 0.6314648144
## 188 1 2 0.6309026523
## 2 1 2 0.6281351952
## 187 1 2 0.6280942859
## 25 1 2 0.6262226793
## 10 1 2 0.6228117939
## 191 1 2 0.6214612147
## 185 1 2 0.6207575058
## 194 1 2 0.6140280156
## 157 1 2 0.6133262354
## 79 1 2 0.6125782169
## 192 1 2 0.6117937989
## 196 1 2 0.6115165228
## 180 1 2 0.6055106429
## 190 1 2 0.6051614396
## 193 1 2 0.5948528369
## 83 1 2 0.5928610004
## 195 1 2 0.5923788332
## 200 1 2 0.5903984120
## 197 1 2 0.5890920799
## 198 1 2 0.5820935105
## 202 1 2 0.5811428669
## 203 1 2 0.5779086739
## 204 1 2 0.5751823941
## 205 1 2 0.5731196615
## 206 1 2 0.5697309551
## 207 1 2 0.5683722821
## 44 1 2 0.5609505292
## 211 1 2 0.5524052160
## 210 1 2 0.5524046065
## 212 1 2 0.5522405342
## 213 1 2 0.5429052769
## 215 1 2 0.5322048248
## 219 1 2 0.5188577741
## 216 1 2 0.5176515556
## 217 1 2 0.5136512042
## 214 1 2 0.5107072561
## 184 1 2 0.5106648013
## 209 1 2 0.5059214706
## 218 1 2 0.5049415995
## 220 1 2 0.5021805488
## 223 1 2 0.5006853105
## 201 1 2 0.4992849344
## 221 1 2 0.4980897687
## 224 1 2 0.4861625791
## 225 1 2 0.4843275442
## 226 1 2 0.4803812700
## 228 1 2 0.4736016417
## 229 1 2 0.4616340029
## 231 1 2 0.4588808775
## 230 1 2 0.4574746869
## 5 1 2 0.4513927883
## 232 1 2 0.4489763140
## 233 1 2 0.4395801626
## 234 1 2 0.4253392463
## 237 1 2 0.4189293811
## 236 1 2 0.4161100555
## 222 1 2 0.4146013342
## 227 1 2 0.4105547936
## 239 1 2 0.4032213908
## 240 1 2 0.4007411677
## 238 1 2 0.3996037367
## 235 1 2 0.3960819703
## 241 1 2 0.3958671544
## 242 1 2 0.3900544908
## 243 1 2 0.3818840853
## 244 1 2 0.3756155404
## 245 1 2 0.3647351665
## 248 1 2 0.3430769043
## 249 1 2 0.3419745837
## 247 1 2 0.3388924610
## 250 1 2 0.3296039887
## 251 1 2 0.3253760638
## 252 1 2 0.3168295044
## 253 1 2 0.3093479749
## 199 1 2 0.3093045793
## 254 1 2 0.2923903566
## 256 1 2 0.2801272332
## 257 1 2 0.2755333979
## 255 1 2 0.2627003437
## 258 1 2 0.2607850609
## 208 1 2 0.2584969218
## 260 1 2 0.2471818002
## 259 1 2 0.2468343230
## 261 1 2 0.2384908983
## 263 1 2 0.2218046497
## 246 1 2 0.2133062187
## 264 1 2 0.2129813668
## 262 1 2 0.1998035432
## 265 1 2 0.1926531822
## 266 1 2 0.1900105921
## 267 1 2 0.1880929985
## 268 1 2 0.1700440915
## 269 1 2 0.1659749702
## 270 1 2 0.1567740197
## 271 1 2 0.1450808239
## 272 1 2 0.1345619116
## 273 1 2 0.1270480930
## 274 1 2 0.1149450986
## 275 1 2 0.1037007021
## 276 1 2 0.0905134813
## 277 1 2 0.0817603761
## 278 1 2 0.0695514498
## 279 1 2 0.0606864954
## 280 1 2 0.0490576758
## 429 2 3 0.7048149219
## 424 2 1 0.7042394020
## 426 2 3 0.7017936439
## 427 2 3 0.7014756811
## 421 2 1 0.6977969177
## 432 2 3 0.6968137551
## 431 2 3 0.6963092407
## 420 2 1 0.6952140911
## 423 2 1 0.6948645525
## 419 2 1 0.6941025718
## 428 2 3 0.6934008671
## 416 2 1 0.6929297986
## 422 2 1 0.6927313934
## 418 2 1 0.6919507550
## 430 2 3 0.6904295065
## 413 2 1 0.6902662343
## 436 2 3 0.6899525736
## 415 2 1 0.6888015137
## 437 2 3 0.6885700094
## 411 2 1 0.6883473745
## 442 2 3 0.6861876804
## 441 2 3 0.6858049944
## 440 2 3 0.6853985283
## 438 2 3 0.6845943054
## 433 2 3 0.6839734445
## 412 2 1 0.6827880750
## 410 2 1 0.6827067185
## 414 2 1 0.6814598600
## 417 2 1 0.6810916545
## 443 2 3 0.6810790508
## 434 2 3 0.6810073049
## 425 2 1 0.6786586528
## 407 2 1 0.6783634200
## 408 2 1 0.6779721384
## 445 2 3 0.6766113354
## 446 2 3 0.6766020634
## 435 2 3 0.6759326454
## 447 2 3 0.6755519822
## 405 2 1 0.6746919533
## 448 2 3 0.6736260546
## 406 2 1 0.6731233483
## 450 2 3 0.6721620466
## 449 2 3 0.6699251205
## 399 2 1 0.6684056070
## 452 2 3 0.6668730203
## 404 2 1 0.6641461720
## 400 2 1 0.6637742098
## 403 2 1 0.6636555036
## 402 2 1 0.6627105792
## 396 2 1 0.6622871157
## 401 2 1 0.6617728954
## 456 2 3 0.6579090124
## 397 2 1 0.6562780999
## 453 2 3 0.6556478607
## 454 2 3 0.6548456138
## 439 2 3 0.6547427742
## 458 2 3 0.6537997947
## 393 2 1 0.6529966318
## 395 2 1 0.6512205323
## 394 2 1 0.6467875325
## 461 2 3 0.6464557411
## 459 2 3 0.6461328365
## 389 2 1 0.6460543924
## 390 2 1 0.6430939733
## 392 2 1 0.6422024581
## 460 2 3 0.6418829164
## 391 2 1 0.6414256808
## 464 2 3 0.6397685339
## 386 2 1 0.6373992592
## 465 2 3 0.6359751350
## 388 2 1 0.6346428261
## 457 2 3 0.6339127543
## 409 2 1 0.6281944020
## 463 2 3 0.6253334137
## 468 2 3 0.6245429660
## 451 2 3 0.6232311530
## 381 2 1 0.6228863228
## 384 2 1 0.6217387400
## 472 2 3 0.6211701232
## 466 2 3 0.6206602028
## 469 2 3 0.6203059930
## 382 2 1 0.6164867030
## 473 2 3 0.6157807529
## 378 2 1 0.6154321591
## 470 2 3 0.6153602812
## 383 2 1 0.6109156040
## 377 2 1 0.6090105489
## 387 2 1 0.6085163810
## 379 2 1 0.6079766074
## 380 2 1 0.6049397794
## 376 2 1 0.6037780420
## 477 2 3 0.6023375394
## 467 2 3 0.5997395070
## 478 2 3 0.5994852441
## 479 2 3 0.5980170857
## 475 2 3 0.5970920898
## 476 2 3 0.5969481554
## 372 2 1 0.5969453762
## 374 2 1 0.5929436400
## 481 2 3 0.5897554365
## 482 2 3 0.5881696779
## 375 2 1 0.5869754792
## 370 2 1 0.5857382560
## 371 2 1 0.5837917506
## 480 2 3 0.5765417981
## 373 2 1 0.5757595379
## 486 2 3 0.5748181698
## 485 2 3 0.5738424325
## 471 2 3 0.5727751841
## 487 2 3 0.5710773762
## 364 2 1 0.5647229187
## 484 2 3 0.5643453529
## 366 2 1 0.5639279707
## 367 2 1 0.5636934942
## 483 2 3 0.5590602175
## 474 2 3 0.5586053795
## 363 2 1 0.5567417426
## 362 2 1 0.5517085225
## 490 2 3 0.5512545570
## 493 2 3 0.5499795803
## 365 2 1 0.5494641622
## 358 2 1 0.5460064252
## 491 2 3 0.5458602946
## 359 2 1 0.5455342746
## 489 2 3 0.5447615734
## 398 2 1 0.5402825665
## 360 2 1 0.5387421334
## 356 2 1 0.5370481973
## 495 2 3 0.5367426609
## 496 2 3 0.5313083132
## 354 2 1 0.5303926758
## 494 2 3 0.5296552680
## 355 2 1 0.5295241162
## 499 2 3 0.5246711622
## 353 2 1 0.5242822655
## 497 2 3 0.5185992860
## 352 2 1 0.5151859108
## 501 2 3 0.5150780919
## 350 2 1 0.5136864540
## 492 2 3 0.5081099064
## 502 2 3 0.5066024812
## 500 2 3 0.5041964768
## 488 2 3 0.5038990023
## 349 2 1 0.4953514132
## 346 2 1 0.4935423765
## 505 2 3 0.4916445123
## 506 2 3 0.4911654833
## 385 2 1 0.4863367517
## 369 2 1 0.4850667385
## 347 2 1 0.4845913951
## 498 2 3 0.4833578030
## 507 2 3 0.4832953206
## 348 2 1 0.4776779235
## 508 2 3 0.4772446796
## 343 2 1 0.4772361787
## 357 2 1 0.4757934523
## 368 2 1 0.4730681271
## 341 2 1 0.4693683937
## 344 2 1 0.4690970119
## 455 2 3 0.4675764934
## 510 2 3 0.4648877128
## 342 2 1 0.4642634014
## 345 2 1 0.4591876673
## 512 2 3 0.4590570067
## 511 2 3 0.4584706544
## 509 2 3 0.4582509712
## 513 2 3 0.4543368075
## 340 2 1 0.4539289316
## 339 2 1 0.4500619773
## 462 2 3 0.4375815534
## 515 2 3 0.4368300734
## 335 2 1 0.4339706650
## 516 2 3 0.4337312216
## 503 2 3 0.4327293693
## 337 2 1 0.4301871175
## 336 2 1 0.4271122929
## 518 2 3 0.4262687007
## 334 2 1 0.4259180298
## 338 2 1 0.4249331702
## 504 2 3 0.4245050760
## 517 2 3 0.4221926484
## 333 2 1 0.4125674566
## 519 2 3 0.4039622554
## 331 2 1 0.4034232623
## 520 2 3 0.4033875408
## 330 2 1 0.4015507666
## 522 2 3 0.4005624799
## 521 2 3 0.3971183889
## 523 2 3 0.3883047348
## 351 2 1 0.3839373236
## 328 2 1 0.3824933402
## 525 2 3 0.3785894886
## 524 2 3 0.3773623914
## 326 2 1 0.3744769795
## 327 2 1 0.3721230981
## 526 2 3 0.3669559351
## 527 2 3 0.3657321849
## 528 2 3 0.3600659559
## 324 2 1 0.3580567765
## 325 2 1 0.3565471876
## 322 2 1 0.3496711928
## 514 2 3 0.3490759284
## 444 2 3 0.3474781042
## 361 2 1 0.3417503939
## 332 2 1 0.3412427919
## 529 2 3 0.3390228030
## 329 2 1 0.3376605103
## 530 2 3 0.3366892608
## 531 2 3 0.3357651666
## 320 2 1 0.3321945974
## 319 2 1 0.3302554514
## 532 2 3 0.3279344331
## 318 2 1 0.3197944305
## 323 2 1 0.3159595133
## 317 2 1 0.3135617001
## 535 2 3 0.3073862237
## 536 2 3 0.3030279690
## 321 2 1 0.3005188273
## 316 2 1 0.2964290011
## 315 2 1 0.2961476126
## 537 2 3 0.2888948819
## 538 2 3 0.2856857024
## 314 2 1 0.2801638166
## 534 2 3 0.2799932201
## 313 2 1 0.2770705799
## 539 2 3 0.2732080521
## 540 2 3 0.2704361069
## 312 2 1 0.2672903653
## 311 2 1 0.2630331783
## 310 2 1 0.2598236554
## 542 2 3 0.2527187786
## 309 2 1 0.2499319490
## 543 2 3 0.2434143826
## 544 2 3 0.2339426797
## 307 2 1 0.2312028571
## 545 2 3 0.2265851292
## 308 2 1 0.2248998416
## 306 2 1 0.2181480429
## 305 2 1 0.2106108633
## 547 2 3 0.2095791845
## 541 2 3 0.2064121467
## 304 2 1 0.2035234967
## 548 2 3 0.1967800131
## 546 2 3 0.1927265437
## 303 2 1 0.1884663856
## 302 2 1 0.1807692385
## 550 2 3 0.1726767348
## 301 2 1 0.1669246277
## 551 2 3 0.1666254611
## 549 2 3 0.1604467025
## 299 2 1 0.1562986242
## 553 2 3 0.1491129038
## 298 2 1 0.1471999897
## 300 2 1 0.1459476770
## 297 2 1 0.1338059872
## 555 2 3 0.1315898935
## 296 2 1 0.1249153878
## 295 2 1 0.1161247021
## 556 2 3 0.1156352986
## 294 2 1 0.1034915396
## 558 2 3 0.0936044782
## 293 2 1 0.0927161700
## 554 2 3 0.0867693303
## 559 2 3 0.0851964873
## 292 2 1 0.0835713912
## 560 2 3 0.0744546834
## 291 2 1 0.0731096819
## 561 2 3 0.0670041178
## 290 2 1 0.0599920941
## 552 2 3 0.0559482429
## 562 2 3 0.0551334404
## 533 2 3 0.0519289034
## 289 2 1 0.0435272602
## 563 2 3 0.0426269358
## 557 2 3 0.0411716490
## 288 2 1 0.0382001149
## 564 2 3 0.0329861459
## 287 2 1 0.0266779050
## 565 2 3 0.0197233273
## 286 2 1 0.0153588310
## 566 2 3 0.0098459070
## 285 2 1 0.0060929131
## 567 2 3 -0.0007751113
## 284 2 1 -0.0084480629
## 283 2 1 -0.0123534441
## 568 2 3 -0.0156220565
## 282 2 1 -0.0162448313
## 569 2 3 -0.0226545342
## 570 2 3 -0.0384696095
## 281 2 1 -0.0437069957
## 571 2 3 -0.0502647663
## 745 3 2 0.7416632919
## 753 3 2 0.7402527124
## 739 3 2 0.7400910727
## 750 3 2 0.7400573509
## 740 3 2 0.7398577312
## 748 3 2 0.7395990155
## 768 3 2 0.7384201119
## 730 3 2 0.7380800554
## 741 3 2 0.7376529890
## 752 3 2 0.7365647152
## 734 3 2 0.7362360686
## 754 3 2 0.7362320136
## 738 3 2 0.7361184418
## 769 3 2 0.7360084516
## 744 3 2 0.7357094243
## 757 3 2 0.7351579175
## 728 3 2 0.7348917201
## 727 3 2 0.7344467665
## 775 3 2 0.7340825167
## 733 3 2 0.7340786521
## 778 3 2 0.7329412201
## 724 3 2 0.7328797686
## 756 3 2 0.7328352968
## 755 3 2 0.7320622956
## 762 3 2 0.7320146172
## 774 3 2 0.7319964922
## 722 3 2 0.7317468104
## 746 3 2 0.7316006497
## 782 3 2 0.7312147717
## 736 3 2 0.7311588283
## 783 3 2 0.7306461420
## 751 3 2 0.7306288979
## 760 3 2 0.7306058870
## 725 3 2 0.7298942425
## 781 3 2 0.7293605081
## 784 3 2 0.7292294242
## 773 3 2 0.7290930490
## 772 3 2 0.7286405032
## 743 3 2 0.7285878915
## 717 3 2 0.7285381577
## 729 3 2 0.7283732043
## 779 3 2 0.7279560615
## 758 3 2 0.7279382453
## 763 3 2 0.7276429144
## 767 3 2 0.7274817644
## 723 3 2 0.7270478036
## 721 3 2 0.7266445560
## 737 3 2 0.7261950141
## 788 3 2 0.7260907033
## 715 3 2 0.7256673800
## 719 3 2 0.7254467548
## 786 3 2 0.7252870679
## 765 3 2 0.7245117120
## 790 3 2 0.7244185397
## 771 3 2 0.7242278626
## 776 3 2 0.7241219297
## 787 3 2 0.7240282539
## 789 3 2 0.7240187710
## 764 3 2 0.7226147663
## 749 3 2 0.7221235838
## 780 3 2 0.7221220667
## 731 3 2 0.7216042748
## 712 3 2 0.7210903551
## 766 3 2 0.7209385656
## 791 3 2 0.7192899922
## 798 3 2 0.7169633581
## 796 3 2 0.7163410872
## 799 3 2 0.7160364339
## 759 3 2 0.7153399609
## 732 3 2 0.7150887750
## 770 3 2 0.7144550269
## 777 3 2 0.7140863188
## 797 3 2 0.7138482600
## 702 3 2 0.7136801951
## 800 3 2 0.7135632129
## 805 3 2 0.7127767044
## 703 3 2 0.7117985138
## 716 3 2 0.7117030618
## 795 3 2 0.7113834266
## 793 3 2 0.7106081183
## 801 3 2 0.7105293274
## 742 3 2 0.7096964334
## 710 3 2 0.7093758524
## 708 3 2 0.7093445294
## 804 3 2 0.7089740203
## 807 3 2 0.7082563564
## 707 3 2 0.7080667002
## 713 3 2 0.7065915454
## 808 3 2 0.7049255750
## 696 3 2 0.7047098545
## 811 3 2 0.7037607545
## 698 3 2 0.7035998923
## 806 3 2 0.7035888699
## 701 3 2 0.7030959718
## 785 3 2 0.7029536189
## 803 3 2 0.7026207664
## 814 3 2 0.7024499813
## 704 3 2 0.7017226756
## 699 3 2 0.7015038121
## 705 3 2 0.7007904922
## 816 3 2 0.7004409256
## 815 3 2 0.6989316507
## 817 3 2 0.6987379027
## 813 3 2 0.6985428353
## 820 3 2 0.6979224820
## 819 3 2 0.6979192516
## 809 3 2 0.6977063900
## 692 3 2 0.6975499412
## 706 3 2 0.6971811815
## 693 3 2 0.6971451535
## 720 3 2 0.6967167081
## 818 3 2 0.6964997671
## 810 3 2 0.6964187440
## 709 3 2 0.6963839958
## 812 3 2 0.6954675598
## 695 3 2 0.6947046682
## 700 3 2 0.6940628735
## 794 3 2 0.6932338349
## 821 3 2 0.6929884992
## 822 3 2 0.6917874823
## 697 3 2 0.6913111173
## 824 3 2 0.6889119996
## 690 3 2 0.6881524765
## 689 3 2 0.6877138992
## 825 3 2 0.6873395113
## 823 3 2 0.6858640508
## 686 3 2 0.6857547304
## 830 3 2 0.6852509497
## 694 3 2 0.6840383700
## 761 3 2 0.6822592706
## 833 3 2 0.6815334710
## 828 3 2 0.6811433979
## 683 3 2 0.6806343628
## 829 3 2 0.6800363795
## 831 3 2 0.6795221182
## 834 3 2 0.6793844606
## 691 3 2 0.6791440817
## 835 3 2 0.6791191707
## 681 3 2 0.6790576683
## 827 3 2 0.6774262056
## 832 3 2 0.6770214934
## 680 3 2 0.6767596035
## 685 3 2 0.6759173484
## 688 3 2 0.6739959264
## 836 3 2 0.6737894992
## 687 3 2 0.6736337361
## 837 3 2 0.6724544056
## 839 3 2 0.6713073662
## 679 3 2 0.6711367349
## 838 3 2 0.6705142718
## 677 3 2 0.6699887557
## 840 3 2 0.6696321542
## 678 3 2 0.6691995061
## 841 3 2 0.6686293822
## 682 3 2 0.6681346778
## 843 3 2 0.6678734206
## 844 3 2 0.6669743879
## 711 3 2 0.6655065771
## 845 3 2 0.6653437308
## 847 3 2 0.6633194716
## 842 3 2 0.6632640403
## 846 3 2 0.6627302695
## 802 3 2 0.6611549367
## 675 3 2 0.6607206574
## 849 3 2 0.6589976014
## 670 3 2 0.6542566699
## 672 3 2 0.6526639770
## 848 3 2 0.6495223926
## 850 3 2 0.6493224544
## 674 3 2 0.6482329689
## 669 3 2 0.6435149358
## 668 3 2 0.6427144867
## 665 3 2 0.6405706182
## 671 3 2 0.6401024213
## 666 3 2 0.6399180159
## 747 3 2 0.6327417515
## 826 3 2 0.6320293116
## 662 3 2 0.6248261160
## 714 3 2 0.6246625955
## 661 3 2 0.6245013182
## 684 3 2 0.6230834384
## 660 3 2 0.6229330738
## 667 3 2 0.6131076381
## 657 3 2 0.6125562261
## 656 3 2 0.6089377116
## 663 3 2 0.6077943908
## 718 3 2 0.6021735698
## 735 3 2 0.6006125699
## 655 3 2 0.5994680456
## 659 3 2 0.5987128916
## 652 3 2 0.5966920846
## 658 3 2 0.5960426287
## 650 3 2 0.5924804298
## 649 3 2 0.5853644738
## 654 3 2 0.5817594481
## 651 3 2 0.5802385844
## 726 3 2 0.5788238094
## 648 3 2 0.5771664808
## 646 3 2 0.5709803283
## 644 3 2 0.5669638082
## 676 3 2 0.5599540840
## 639 3 2 0.5503251930
## 642 3 2 0.5496623740
## 641 3 2 0.5445916792
## 645 3 2 0.5427479723
## 664 3 2 0.5399722292
## 637 3 2 0.5395285087
## 636 3 2 0.5295901004
## 634 3 2 0.5224404570
## 632 3 2 0.5170911629
## 640 3 2 0.5167400106
## 638 3 2 0.5097755358
## 653 3 2 0.5062743573
## 633 3 2 0.5029059436
## 631 3 2 0.5001866827
## 627 3 2 0.4885540450
## 629 3 2 0.4871144190
## 626 3 2 0.4863945057
## 628 3 2 0.4825427359
## 625 3 2 0.4712871534
## 624 3 2 0.4665707594
## 623 3 2 0.4651681215
## 622 3 2 0.4643668903
## 620 3 2 0.4570009698
## 619 3 2 0.4504993645
## 635 3 2 0.4477168709
## 647 3 2 0.4430176507
## 618 3 2 0.4426346360
## 617 3 2 0.4364196322
## 616 3 2 0.4331444991
## 621 3 2 0.4273843530
## 615 3 2 0.4228083552
## 614 3 2 0.4193357346
## 612 3 2 0.4047518179
## 613 3 2 0.4044777549
## 611 3 2 0.3992912789
## 610 3 2 0.3950562307
## 609 3 2 0.3856216613
## 608 3 2 0.3795949084
## 607 3 2 0.3716998788
## 630 3 2 0.3713266675
## 605 3 2 0.3572322300
## 606 3 2 0.3564713509
## 792 3 2 0.3452460382
## 673 3 2 0.3359964601
## 602 3 2 0.3353540944
## 643 3 2 0.3322339744
## 603 3 2 0.3320795168
## 601 3 2 0.3299627389
## 604 3 2 0.3254478963
## 600 3 2 0.3202725022
## 598 3 2 0.2962233049
## 597 3 2 0.2874025340
## 596 3 2 0.2867647892
## 599 3 2 0.2825624151
## 594 3 2 0.2716512382
## 595 3 2 0.2683906918
## 593 3 2 0.2612115289
## 591 3 2 0.2480003795
## 590 3 2 0.2286727813
## 589 3 2 0.2279665166
## 592 3 2 0.2258391823
## 588 3 2 0.2191353548
## 587 3 2 0.2103358308
## 586 3 2 0.1908361377
## 585 3 2 0.1900729421
## 584 3 2 0.1815464928
## 583 3 2 0.1721128054
## 582 3 2 0.1618672168
## 581 3 2 0.1420352072
## 580 3 2 0.1368256762
## 579 3 2 0.1305669012
## 578 3 2 0.1206903077
## 577 3 2 0.1085001932
## 576 3 2 0.0979783381
## 575 3 2 0.0883789197
## 574 3 2 0.0763775654
## 573 3 2 0.0663045202
## 572 3 2 0.0150286840
## Average silhouette width per cluster:
## [1] 0.5928479 0.4447609 0.5946901
## Average silhouette width of total data set:
## [1] 0.5427545
##
## Available components:
## [1] "membership" "coeff" "memb.exp" "clustering" "k.crisp"
## [6] "objective" "convergence" "diss" "call" "silinfo"
## [11] "data"head(cliente_agrupa$clustering)## [1] 1 1 1 1 1 1plot(cliente_agrupa)
En el gráfico existe un 68.86% de variabilidad entre los puntos. En la silhouette se tiene un 0.34 Para obtener una representación gráfica del clustering se puede emplear la función fviz_cluster().
library(factoextra)
fviz_cluster(object = cliente_agrupa, repel = TRUE, ellipse.type = "norm",
pallete = "jco") + theme_bw() + labs(title = "Fuzzy Cluster plot")