# Clustering Example: 4 Steps You Should Know #### Clustering Example: 4 Steps You Should Know

This article describes k-means clustering example and provide a step-by-step guide summarizing the different steps to follow for conducting a cluster analysis on a real data set using R software.

We’ll use mainly two R packages:

• cluster: for cluster analyses and
• factoextra: for the visualization of the analysis results.

Install these packages, as follow:

install.packages(c("cluster", "factoextra"))

A rigorous cluster analysis can be conducted in 3 steps mentioned below:

Here, we provide quick R scripts to perform all these steps.

Contents:

#### Related Book

Practical Guide to Cluster Analysis in R

## Data preparation

We’ll use the demo data set USArrests. We start by standardizing the data using the scale() function:

# Load the data set
data(USArrests)
# Standardize
df <- scale(USArrests)

## Assessing the clusterability

The function get_clust_tendency() [factoextra package] can be used. It computes the Hopkins statistic and provides a visual approach.

library("factoextra")
res <- get_clust_tendency(df, 40, graph = TRUE)
# Hopskin statistic
res$hopkins_stat ##  0.656 # Visualize the dissimilarity matrix print(res$plot) The value of the Hopkins statistic is significantly < 0.5, indicating that the data is highly clusterable. Additionally, It can be seen that the ordered dissimilarity image contains patterns (i.e., clusters).

## Estimate the number of clusters in the data

As k-means clustering requires to specify the number of clusters to generate, we’ll use the function clusGap() [cluster package] to compute gap statistics for estimating the optimal number of clusters . The function fviz_gap_stat() [factoextra] is used to visualize the gap statistic plot.

library("cluster")
set.seed(123)
# Compute the gap statistic
gap_stat <- clusGap(df, FUN = kmeans, nstart = 25,
K.max = 10, B = 100)
# Plot the result
library(factoextra)
fviz_gap_stat(gap_stat) The gap statistic suggests a 4 cluster solutions.

It’s also possible to use the function NbClust() [in NbClust] package.

## Compute k-means clustering

K-means clustering with k = 4:

# Compute k-means
set.seed(123)
km.res <- kmeans(df, 4, nstart = 25)
head(km.res$cluster, 20) ## Alabama Alaska Arizona Arkansas California Colorado ## 4 3 3 4 3 3 ## Connecticut Delaware Florida Georgia Hawaii Idaho ## 2 2 3 4 2 1 ## Illinois Indiana Iowa Kansas Kentucky Louisiana ## 3 2 1 2 1 4 ## Maine Maryland ## 1 3 # Visualize clusters using factoextra fviz_cluster(km.res, USArrests) ## Cluster validation statistics: Inspect cluster silhouette plot Recall that the silhouette measures ($S_i$) how similar an object $i$ is to the the other objects in its own cluster versus those in the neighbor cluster. $S_i$ values range from 1 to - 1: • A value of $S_i$ close to 1 indicates that the object is well clustered. In the other words, the object $i$ is similar to the other objects in its group. • A value of $S_i$ close to -1 indicates that the object is poorly clustered, and that assignment to some other cluster would probably improve the overall results. sil <- silhouette(km.res$cluster, dist(df))
rownames(sil) <- rownames(USArrests)
head(sil[, 1:3])
##            cluster neighbor sil_width
## Alabama          4        3    0.4858
## Alaska           3        4    0.0583
## Arizona          3        2    0.4155
## Arkansas         4        2    0.1187
## California       3        2    0.4356
## Colorado         3        2    0.3265
fviz_silhouette(sil)
##   cluster size ave.sil.width
## 1       1   13          0.37
## 2       2   16          0.34
## 3       3   13          0.27
## 4       4    8          0.39 It can be seen that there are some samples which have negative silhouette values. Some natural questions are :

Which samples are these? To what cluster are they closer?

This can be determined from the output of the function silhouette() as follow:

neg_sil_index <- which(sil[, "sil_width"] < 0)
sil[neg_sil_index, , drop = FALSE]
##          cluster neighbor sil_width
## Missouri       3        2   -0.0732

## eclust(): Enhanced clustering analysis

The function eclust()[factoextra package] provides several advantages compared to the standard packages used for clustering analysis:

• It simplifies the workflow of clustering analysis
• It can be used to compute hierarchical clustering and partitioning clustering in a single line function call
• The function eclust() computes automatically the gap statistic for estimating the right number of clusters.
• It automatically provides silhouette information
• It draws beautiful graphs using ggplot2

### K-means clustering using eclust()

# Compute k-means
res.km <- eclust(df, "kmeans", nstart = 25) # Gap statistic plot
fviz_gap_stat(res.km\$gap_stat) # Silhouette plot
fviz_silhouette(res.km) ### Hierachical clustering using eclust()

 # Enhanced hierarchical clustering
res.hc <- eclust(df, "hclust") # compute hclust
## Clustering k = 1,2,..., K.max (= 10): .. done
## Bootstrapping, b = 1,2,..., B (= 100)  [one "." per sample]:
## .................................................. 50
## .................................................. 100
fviz_dend(res.hc, rect = TRUE) # dendrogam The R code below generates the silhouette plot and the scatter plot for hierarchical clustering.

fviz_silhouette(res.hc) # silhouette plot
fviz_cluster(res.hc) # scatter plot

### Comments ( 2 )

• Gary Napier

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