03-02-Hierarchical-Clustering.Rmd

## Hierarchical Clustering {-}

```{r a2, warning=FALSE, message=FALSE, comment=NA, fig.width=8, fig.height=7}

library(cluster)

## Hierarchical clustering using the mtcars dataset
##
## Data description from R:
## The data was extracted from the 1974 Motor Trend US magazine, and comprises
## fuel consumption and 10 aspects of automobile design and performance for
## 32 automobiles (1973-74 models).
##

## Since the attributes have different scales we need to standardize them
cars.dist = dist(scale(mtcars, center = TRUE, scale = TRUE))

## Clustering by rows (cars)
## Method Average, average distance between all points in a cluster
plot(hclust(cars.dist, method = "average"), xlab = "", ylab ="Distance")

## Method Single, shortest distance between each cluster
plot(hclust(cars.dist, method = "single"), xlab = "", ylab ="Distance")

## Method Complete, longest distance between each cluster
plot(hclust(cars.dist, method = "complete"), xlab = "", ylab ="Distance")

## Method Ward, minimizes the loss of exmplained variance
plot(hclust(cars.dist, method = "ward.D"), xlab = "", ylab ="Distance")

## Attribute Descriptions:
##
## mpg	 Miles/(US) gallon
## cyl	 Number of cylinders
## disp	 Displacement (cu.in.)
## hp	 Gross horsepower
## drat	 Rear axle ratio
## wt	 Weight (lb/1000)
## qsec	 1/4 mile time
## vs	 V/S
## am	 Transmission (0 = automatic, 1 = manual)
## gear	 Number of forward gears
## carb	 Number of carburetors

## Data and Summary
mtcars; summary(mtcars)

```