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K-means introduction in R: code used for post: http://btovar.com/2015/04/introduction-to-k-means-in-r/
# ***********************************************
# ***********************************************
# Author: Benjamin Tovar
# Date: April 20, 2015
# Post: http://btovar.com/2015/04/introduction-to-k-means-in-r/ ‎
# ***********************************************
# ***********************************************
library(ggplot2)
library(reshape)
#####################################################
# Case study 1: the basics of the K-means in R
#####################################################
set.seed(1121)
## Set three vectors, each with 100 entries
l <- 100
## x with a mean of 1 and a sd of 0.5
x <- rnorm(l,1,0.5)
mean(x);var(x);sd(x);summary(x)
## y with a mean of 5 and a sd of 1
y <- rnorm(l,5,1)
mean(y);var(y);sd(y);summary(y)
## z with a mean of 4 and a sd of 0.4
z <- rnorm(l,4,0.4)
mean(z);var(z);sd(z);summary(z)
# set dataset
dataset <- data.frame(x=x,y=y,z=z)
dataset_m <- melt(dataset)
dataset_m <- cbind(id=1:(l*3),dataset_m)
colnames(dataset_m) <- c("id","class","value")
# Now plot the distribution of the artificial dataset in order to check out the
# distribution of the entries in the dataset:
ggplot(dataset_m) +
aes(x=value) +
geom_density(aes(fill="red",colour="red"),alpha=0.4) +
theme(legend.position="none") +
labs(title="Density plot of all values (omitting classes)")
ggplot(dataset_m) +
aes(x=value) +
geom_density(aes(fill=class,colour=class),alpha=0.4) +
labs(title="Density plot for all classes")
# Scatter plot of dataset
ggplot(dataset_m) +
aes(x=id,y=value) +
geom_point(aes(shape=class),size=2.5,alpha=0.9) +
labs(title="Scatter plot of dataset")
## set the number of centroids with parameter k
k <- 2
## run the Kmeans algorithm
km <- kmeans(dataset_m$value,centers=k)
dataset_km <- cbind(dataset_m,cluster=as.factor(km$cluster))
# plot each sample
ggplot(dataset_km) +
aes(x=id,y=value) +
geom_point(aes(shape=class,colour=cluster),size=2.5,alpha=0.9) +
labs(title="Dataset K-means clustering | k=2")
## set the number of centroids with parameter k
k <- 3
## run the Kmeans algorithm
km <- kmeans(dataset_m$value,centers=k)
dataset_km <- cbind(dataset_m,cluster=as.factor(km$cluster))
# plot each sample
ggplot(dataset_km) +
aes(x=id,y=value) +
geom_point(aes(shape=class,colour=cluster),size=2.5,alpha=0.9) +
labs(title="Dataset K-means clustering | k=3")
## set the number of centroids with parameter k
k <- 4
## run the Kmeans algorithm
km <- kmeans(dataset_m$value,centers=k)
dataset_km <- cbind(dataset_m,cluster=as.factor(km$cluster))
# plot each sample
ggplot(dataset_km) +
aes(x=id,y=value) +
geom_point(aes(shape=class,colour=cluster),size=2.5,alpha=0.9) +
labs(title="Dataset K-means clustering | k=4")
##############
# COMPUTE THE BEST VALUE
# FOR PARAMETER K
##############
# TRAIN THE MODEL ITERATING THE VALUES OF K
max_k_size <- 10
error <- numeric(max_k_size)
for (i in 1:max_k_size){
error[i] <- sum(kmeans(dataset,centers=i)$withinss)
}
# set data frame object
error <- data.frame(k=1:max_k_size,error=error)
ggplot(error) +
aes(x=k,y=error) +
geom_point(colour="#f96161",size=3) +
ylim(0,150) +
scale_x_continuous(breaks=seq(0, 15, 1)) +
geom_line(colour="#f96161") +
labs(title="Error calibration curve",
x="Number of Clusters (k)",
y="Within groups sum of squares") +
geom_vline(xintercept=3,colour="#32ab9f", linetype = "longdash")
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