Source Link: https://datageneration.org/gentlemachinelearning/module4_unsupervisedlearning
# install and load pacman for package management
if (!require("pacman", character.only = TRUE)) install.packages("pacman")Loading required package: pacmanWarning: package 'pacman' was built under R version 4.2.3library(pacman)
# load libraries using pacman
p_load("datasets",
"ISLR",
"dplyr",
"ggplot2",
"RColorBrewer",
"factoextra",
"animation",
"readr")## Gentle Machine Learning
## Principal Component Analysis
# Dataset: USArrests is the sample dataset used in
# McNeil, D. R. (1977) Interactive Data Analysis. New York: Wiley.
# Murder numeric Murder arrests (per 100,000)
# Assault numeric Assault arrests (per 100,000)
# UrbanPop numeric Percent urban population
# Rape numeric Rape arrests (per 100,000)
# For each of the fifty states in the United States, the dataset contains the number
# of arrests per 100,000 residents for each of three crimes: Assault, Murder, and Rape.
# UrbanPop is the percent of the population in each state living in urban areas.
arrest = USArrests
states=row.names(USArrests)
names(USArrests)[1] "Murder" "Assault" "UrbanPop" "Rape" # Get means and variances of variables
apply(USArrests, 2, mean) Murder Assault UrbanPop Rape
7.788 170.760 65.540 21.232 apply(USArrests, 2, var) Murder Assault UrbanPop Rape
18.97047 6945.16571 209.51878 87.72916 # PCA with scaling
pr.out=prcomp(USArrests, scale=TRUE)
names(pr.out) # Five[1] "sdev" "rotation" "center" "scale" "x" pr.out$center # the centering and scaling used (means) Murder Assault UrbanPop Rape
7.788 170.760 65.540 21.232 pr.out$scale # the matrix of variable loadings (eigenvectors) Murder Assault UrbanPop Rape
4.355510 83.337661 14.474763 9.366385 pr.out$rotation PC1 PC2 PC3 PC4
Murder -0.5358995 0.4181809 -0.3412327 0.64922780
Assault -0.5831836 0.1879856 -0.2681484 -0.74340748
UrbanPop -0.2781909 -0.8728062 -0.3780158 0.13387773
Rape -0.5434321 -0.1673186 0.8177779 0.08902432dim(pr.out$x)[1] 50 4pr.out$rotation=-pr.out$rotation
pr.out$x=-pr.out$x
biplot(pr.out, scale=0)
pr.out$sdev[1] 1.5748783 0.9948694 0.5971291 0.4164494pr.var=pr.out$sdev^2
pr.var[1] 2.4802416 0.9897652 0.3565632 0.1734301pve=pr.var/sum(pr.var)
pve[1] 0.62006039 0.24744129 0.08914080 0.04335752plot(pve, xlab="Principal Component", ylab="Proportion of Variance Explained", ylim=c(0,1),type='b')
plot(cumsum(pve), xlab="Principal Component", ylab="Cumulative Proportion of Variance Explained", ylim=c(0,1),type='b')
## Use factoextra package
fviz(pr.out, "ind", geom = "auto", mean.point = TRUE, font.family = "Georgia")Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
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## Computer purchase example: Animated illustration
## Adapted from Guru99 tutorial (https://www.guru99.com/r-k-means-clustering.html)
## Dataset: characteristics of computers purchased.
## Variables used: RAM size, Harddrive size
computers = read.csv("https://raw.githubusercontent.com/guru99-edu/R-Programming/master/computers.csv")
# Only retain two variables for illustration
rescaled_comp <- computers[4:5] %>%
mutate(hd_scal = scale(hd),
ram_scal = scale(ram)) %>%
select(c(hd_scal, ram_scal))
ggplot(data = rescaled_comp, aes(x = hd_scal, y = ram_scal)) +
geom_point(pch=20, col = "darkblue") + theme_bw() +
labs(x = "Hard drive size (Scaled)", y ="RAM size (Scaled)" ) +
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# install.packages("animation")
set.seed(2345)
# Animate the K-mean clustering process, cluster no. = 4
kmeans.ani(rescaled_comp[1:2], centers = 4, pch = 15:18, col = 1:4) 
saveGIF(
kmeans.ani(rescaled_comp[1:2], centers = 4, pch = 15:18, col = 1:4) ,
movie.name = "kmeans_animated.gif",
img.name = "kmeans",
convert = "magick",
cmd.fun,
clean = TRUE,
extra.opts = ""
)Output at: kmeans_animated.gif[1] TRUE## Iris example
# Without grouping by species
ggplot(iris, aes(Petal.Length, Petal.Width)) + geom_point() +
theme_bw() +
scale_color_manual(values=c("firebrick1","forestgreen","darkblue"))
# With grouping by species
ggplot(iris, aes(Petal.Length, Petal.Width, color = Species)) + geom_point() +
theme_bw() +
scale_color_manual(values=c("firebrick1","forestgreen","darkblue"))
# Check k-means clusters
## Starting with three clusters and 20 initial configurations
set.seed(20)
irisCluster <- kmeans(iris[, 3:4], 3, nstart = 20)
irisClusterK-means clustering with 3 clusters of sizes 52, 48, 50
Cluster means:
Petal.Length Petal.Width
1 4.269231 1.342308
2 5.595833 2.037500
3 1.462000 0.246000
Clustering vector:
[1] 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 3 3
[38] 3 3 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[75] 1 1 1 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 1 2 2 2 2
[112] 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2
[149] 2 2
Within cluster sum of squares by cluster:
[1] 13.05769 16.29167 2.02200
(between_SS / total_SS = 94.3 %)
Available components:
[1] "cluster" "centers" "totss" "withinss" "tot.withinss"
[6] "betweenss" "size" "iter" "ifault" class(irisCluster$cluster)[1] "integer"# Confusion matrix
table(irisCluster$cluster, iris$Species)
setosa versicolor virginica
1 0 48 4
2 0 2 46
3 50 0 0irisCluster$cluster <- as.factor(irisCluster$cluster)
ggplot(iris, aes(Petal.Length, Petal.Width, color = irisCluster$cluster)) + geom_point() +
scale_color_manual(values=c("firebrick1","forestgreen","darkblue")) +
theme_bw()
actual = ggplot(iris, aes(Petal.Length, Petal.Width, color = Species)) + geom_point() +
theme_bw() +
scale_color_manual(values=c("firebrick1","forestgreen","darkblue")) +
theme(legend.position="bottom") +
theme(text = element_text(family="Georgia"))
kmc = ggplot(iris, aes(Petal.Length, Petal.Width, color = irisCluster$cluster)) + geom_point() +
theme_bw() +
scale_color_manual(values=c("firebrick1", "darkblue", "forestgreen")) +
theme(legend.position="bottom") +
theme(text = element_text(family="Georgia"))
library(grid)
library(gridExtra)Warning: package 'gridExtra' was built under R version 4.2.3
Attaching package: 'gridExtra'The following object is masked from 'package:dplyr':
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## Wine example
# The wine dataset contains the results of a chemical analysis of wines
# grown in a specific area of Italy. Three types of wine are represented in the
# 178 samples, with the results of 13 chemical analyses recorded for each sample.
# Variables used in this example:
# Alcohol
# Malic: Malic acid
# Ash
# Source: http://archive.ics.uci.edu/ml/datasets/Wine
# Import wine dataset
wine <- read.csv("https://raw.githubusercontent.com/datageneration/gentlemachinelearning/master/data/wine.csv")
## Choose and scale variables
wine_subset <- scale(wine[ , c(2:4)])
## Create cluster using k-means, k = 3, with 25 initial configurations
wine_cluster <- kmeans(wine_subset, centers = 3,
iter.max = 10,
nstart = 25)
wine_clusterK-means clustering with 3 clusters of sizes 48, 60, 70
Cluster means:
Alcohol Malic Ash
1 0.1470536 1.3907328 0.2534220
2 0.8914655 -0.4522073 0.5406223
3 -0.8649501 -0.5660390 -0.6371656
Clustering vector:
[1] 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2
[38] 2 3 1 2 1 2 1 3 1 1 2 2 2 3 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 2 3 3 2 2 2
[75] 3 3 3 3 3 1 3 3 3 1 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
[112] 3 1 3 3 3 3 3 1 3 3 2 1 1 1 3 3 3 3 1 3 1 3 1 3 3 1 1 1 1 1 2 1 1 1 1 1 1
[149] 1 1 1 1 2 1 3 1 1 1 2 2 1 1 1 1 2 1 1 1 2 1 3 3 2 1 1 1 2 1
Within cluster sum of squares by cluster:
[1] 73.71460 67.98619 111.63512
(between_SS / total_SS = 52.3 %)
Available components:
[1] "cluster" "centers" "totss" "withinss" "tot.withinss"
[6] "betweenss" "size" "iter" "ifault" # Create a function to compute and plot total within-cluster sum of square (within-ness)
wssplot <- function(data, nc=15, seed=1234){
wss <- (nrow(data)-1)*sum(apply(data,2,var))
for (i in 2:nc){
set.seed(seed)
wss[i] <- sum(kmeans(data, centers=i)$withinss)}
plot(1:nc, wss, type="b", xlab="Number of Clusters",
ylab="Within groups sum of squares")
}
# plotting values for each cluster starting from 1 to 9
wssplot(wine_subset, nc = 9)
# Plot results by dimensions
wine_cluster$cluster = as.factor(wine_cluster$cluster)
pairs(wine[2:4],
col = c("firebrick1", "darkblue", "forestgreen")[wine_cluster$cluster],
pch = c(15:17)[wine_cluster$cluster],
main = "K-Means Clusters: Wine data")
table(wine_cluster$cluster)
1 2 3
48 60 70 ## Use the factoextra package to do more
# install.packages("factoextra")
fviz_nbclust(wine_subset, kmeans, method = "wss")
# Use eclust() procedure to do K-Means
wine.km <- eclust(wine_subset, "kmeans", nboot = 2)
# Print result
wine.kmK-means clustering with 3 clusters of sizes 60, 70, 48
Cluster means:
Alcohol Malic Ash
1 0.8914655 -0.4522073 0.5406223
2 -0.8649501 -0.5660390 -0.6371656
3 0.1470536 1.3907328 0.2534220
Clustering vector:
[1] 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 1 3 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1
[38] 1 2 3 1 3 1 3 2 3 3 1 1 1 2 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 1 2 2 1 1 1
[75] 2 2 2 2 2 3 2 2 2 3 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
[112] 2 3 2 2 2 2 2 3 2 2 1 3 3 3 2 2 2 2 3 2 3 2 3 2 2 3 3 3 3 3 1 3 3 3 3 3 3
[149] 3 3 3 3 1 3 2 3 3 3 1 1 3 3 3 3 1 3 3 3 1 3 2 2 1 3 3 3 1 3
Within cluster sum of squares by cluster:
[1] 67.98619 111.63512 73.71460
(between_SS / total_SS = 52.3 %)
Available components:
[1] "cluster" "centers" "totss" "withinss" "tot.withinss"
[6] "betweenss" "size" "iter" "ifault" "clust_plot"
[11] "silinfo" "nbclust" "data" "gap_stat" # Optimal number of clusters using gap statistics
wine.km$nbclust[1] 3fviz_nbclust(wine_subset, kmeans, method = "gap_stat")
# Silhouette plot
fviz_silhouette(wine.km) cluster size ave.sil.width
1 1 60 0.44
2 2 70 0.33
3 3 48 0.30
fviz_cluster(wine_cluster, data = wine_subset) +
theme_bw() +
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fviz_cluster(wine_cluster, data = wine_subset, ellipse.type = "norm") +
theme_bw() +
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## Hierarchical Clustering
## Dataset: USArrests
# install.packages("cluster")
arrest.hc <- USArrests %>%
scale() %>% # Scale all variables
dist(method = "euclidean") %>% # Euclidean distance for dissimilarity
hclust(method = "ward.D2") # Compute hierarchical clustering
# Generate dendrogram using factoextra package
fviz_dend(arrest.hc, k = 4, # Four groups
cex = 0.5,
k_colors = c("firebrick1","forestgreen","blue", "purple"),
color_labels_by_k = TRUE, # color labels by groups
rect = TRUE, # Add rectangle (cluster) around groups,
main = "Cluster Dendrogram: USA Arrest data"
) + theme(text = element_text(family="Georgia")) Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
of ggplot2 3.3.4.
ℹ The deprecated feature was likely used in the factoextra package.
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For our tweet data group project, the dendrogram could certainly help highlight clustering beyond sentiment to tell a clearer picture of associated words in tweets.