Data Clustering with R

Data Clustering with R
Yanchang Zhao
http://www.RDataMining.com
30 September 2014
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Outline
Introduction
The k-Means Clustering
The k-Medoids Clustering
Hierarchical Clustering
Density-based Clustering
Online Resources
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Data Clustering with R 1
I k-means clustering with kmeans()
I k-medoids clustering with pam() and pamk()
I hierarchical clustering
I density-based clustering with DBSCAN
1Chapter 6: Clustering, in book R and Data Mining: Examples and Case
Studies. http://www.rdatamining.com/docs/RDataMining.pdf
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Outline
Introduction
Online Resources
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k-means clustering
set.seed(8953)
iris2 <- iris
iris2$Species <- NULL
(kmeans.result <- kmeans(iris2, 3))
## K-means clustering with 3 clusters of sizes 38, 50, 62
##
## Cluster means:
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 6.850 3.074 5.742 2.071
## 2 5.006 3.428 1.462 0.246
## 3 5.902 2.748 4.394 1.434
##
## Clustering vector:
## [1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2...
## [31] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 1 3 3 3 3...
## [61] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3...
## [91] 3 3 3 3 3 3 3 3 3 3 1 3 1 1 1 1 3 1 1 1 1 1 1 3 3 1 1...
## [121] 1 3 1 3 1 1 3 3 1 1 1 1 1 3 1 1 1 1 3 1 1 1 3 1 1 1 3...
##
## Within cluster sum of squares by cluster:
## [1] 23.88 15.15 39.82
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Results of k-Means Clustering
Check clustering result against class labels (Species)
table(iris$Species, kmeans.result$cluster)
##
## 1 2 3
## setosa 0 50 0
## versicolor 2 0 48
## virginica 36 0 14
I Class setosa" can be easily separated from the other clusters
I Classes versicolor" and virginica" are to a small degree
overlapped with each other.
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plot(iris2[c("Sepal.Length", "Sepal.Width")], col = kmeans.result$cluster)
points(kmeans.result$centers[, c("Sepal.Length", "Sepal.Width")],
col = 1:3, pch = 8, cex = 2) # plot cluster centers
4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0
2.0 2.5 3.0 3.5 4.0
Sepal.Length
Sepal.Width
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Outline
Introduction
Online Resources
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I Dierence from k-means: a cluster is represented with its
center in the k-means algorithm, but with the object closest
to the center of the cluster in the k-medoids clustering.
I more robust than k-means in presence of outliers
I PAM (Partitioning Around Medoids) is a classic algorithm for
k-medoids clustering.
I The CLARA algorithm is an enhanced technique of PAM by
drawing multiple samples of data, applying PAM on each
sample and then returning the best clustering. It performs
better than PAM on larger data.
I Functions pam() and clara() in package cluster
I Function pamk() in package fpc does not require a user to
choose k.
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Clustering with pamk()
library(fpc)
pamk.result - pamk(iris2)
# number of clusters
pamk.result$nc
## [1] 2
# check clustering against actual species
table(pamk.result$pamobject$clustering, iris$Species)
##
## setosa versicolor virginica
## 1 50 1 0
## 2 0 49 50
Two clusters:
I setosa
I a mixture of versicolor and virginica
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layout(matrix(c(1, 2), 1, 2)) # 2 graphs per page
plot(pamk.result$pamobject)
clusplot(pam(x = sdata, k = k, diss = diss))
−3 −1 0 1 2 3 4
−2 −1 0 1 2 3
Component 1 Component 2
Silhouette plot of pam(x = sdata, k = n = 150 2 clusters Cj
Average silhouette width : 0.69
0.0 0.2 0.4 0.6 0.8 1.0
Silhouette width si
These two components explain 95.81 % of the point variability.
j : nj | aveiÎCj si
1 : 51 | 0.81
2 : 99 | 0.62
layout(matrix(1)) # change back to one graph per page 11 / 30

I The left chart is a 2-dimensional clusplot (clustering plot)
of the two clusters and the lines show the distance between
clusters.
I The right chart shows their silhouettes. A large si (almost 1)
suggests that the corresponding observations are very well
clustered, a small si (around 0) means that the observation
lies between two clusters, and observations with a negative si
are probably placed in the wrong cluster.
I Since the average Si are respectively 0.81 and 0.62 in the
above silhouette, the identi

ed two clusters are well clustered.
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Clustering with pam()
# group into 3 clusters
pam.result - pam(iris2, 3)
table(pam.result$clustering, iris$Species)
##
## 1 50 0 0
## 2 0 48 14
## 3 0 2 36
Three clusters:
I Cluster 1 is species setosa and is well separated from the
other two.
I Cluster 2 is mainly composed of versicolor, plus some cases
from virginica.
I The majority of cluster 3 are virginica, with two cases from
versicolor.
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layout(matrix(c(1, 2), 1, 2)) # 2 graphs per page
plot(pam.result)
clusplot(pam(x = iris2, k = 3))
−3 −2 −1 0 1 2 3
−3 −2 −1 0 1 2
Component 1 Component 2
Silhouette plot of pam(x = iris2, k = 3)
n = 150 3 clusters Cj
j : nj | aveiÎCj si
1 : 50 | 0.80
2 : 62 | 0.42
3 : 38 | 0.45
0.0 0.2 0.4 0.6 0.8 1.0
Silhouette width si
These two components explain 95.81 % of the point variability.
Average silhouette width : 0.55
layout(matrix(1)) # change back to one graph per page 14 / 30

Results of Clustering
I In this example, the result of pam() seems better, because it
identi

es three clusters, corresponding to three species.
I Note that we cheated by setting k = 3 when using pam(),
which is already known to us as the number of species.
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Outline
Introduction
Online Resources
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Hierarchical Clustering of the iris Data
set.seed(2835)
# draw a sample of 40 records from the iris data, so that the
# clustering plot will not be over crowded
idx - sample(1:dim(iris)[1], 40)
irisSample - iris[idx, ]
# remove class label
irisSample$Species - NULL
# hierarchical clustering
hc - hclust(dist(irisSample), method = ave)
# plot clusters
plot(hc, hang = -1, labels = iris$Species[idx])
# cut tree into 3 clusters
rect.hclust(hc, k = 3)
# get cluster IDs
groups - cutree(hc, k = 3)
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setosa
setosa
setosa
setosa
setosa
setosa
setosa
setosa
setosa
setosa
setosa
setosa
setosa
setosa
versicolor
versicolor
versicolor
virginica
virginica
versicolor
versicolor
versicolor
versicolor
versicolor
versicolor
versicolor
versicolor
versicolor
versicolor
versicolor
versicolor
versicolor
virginica
virginica
virginica
virginica
virginica
virginica
virginica
virginica
0 1 2 3 4
Cluster Dendrogram
hclust (*, average)
dist(irisSample)
Height
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Outline
Introduction
Online Resources
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I Group objects into one cluster if they are connected to one
another by densely populated area
I The DBSCAN algorithm from package fpc provides a
density-based clustering for numeric data.
I Two key parameters in DBSCAN:
I eps: reachability distance, which de

nes the size of
neighborhood; and
I MinPts: minimum number of points.
I If the number of points in the neighborhood of point is no
less than MinPts, then is a dense point. All the points in its
neighborhood are density-reachable from and are put into
the same cluster as .
I Can discover clusters with various shapes and sizes
I Insensitive to noise
I The k-means algorithm tends to

nd clusters with sphere
shape and with similar sizes.
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Density-based Clustering of the iris data
library(fpc)
iris2 - iris[-5] # remove class tags
ds - dbscan(iris2, eps = 0.42, MinPts = 5)
# compare clusters with original class labels
table(ds$cluster, iris$Species)
##
## 0 2 10 17
## 1 48 0 0
## 2 0 37 0
## 3 0 3 33
I 1 to 3: identi

ed clusters
I 0: noises or outliers, i.e., objects that are not assigned to any
clusters
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plot(ds, iris2)
Sepal.Length
2.0 2.5 3.0 3.5 4.0 0.5 1.0 1.5 2.0 2.5
4.5 5.5 6.5 7.5
2.0 2.5 3.0 3.5 4.0
Sepal.Width
Petal.Length
1 2 3 4 5 6 7
4.5 5.5 6.5 7.5
0.5 1.0 1.5 2.0 2.5
1 2 3 4 5 6 7
Petal.Width
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plot(ds, iris2[c(1, 4)])
4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0
0.5 1.0 1.5 2.0 2.5
Sepal.Length
Petal.Width
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plotcluster(iris2, ds$cluster)
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−8 −6 −4 −2 0 2
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Prediction with Clustering Model
I Label new data, based on their similarity with the clusters
I Draw a sample of 10 objects from iris and add small noises
to them to make a new dataset for labeling
I Random noises are generated with a uniform distribution
using function runif().
# create a new dataset for labeling
set.seed(435)
idx - sample(1:nrow(iris), 10)
# remove class labels
new.data - iris[idx,-5]
# add random noise
new.data - new.data + matrix(runif(10*4, min=0, max=0.2),
nrow=10, ncol=4)
# label new data
pred - predict(ds, iris2, new.data)
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Results of Prediction
table(pred, iris$Species[idx]) # check cluster labels
##
## pred setosa versicolor virginica
## 0 0 0 1
## 1 3 0 0
## 2 0 3 0
## 3 0 1 2
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Data Clustering with R

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