# Clustering

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Hello, everyone! I’ve been meaning to get a new blog post out for the

past couple of weeks. During that time I’ve been messing around with

clustering. Clustering, or cluster analysis, is a method of data mining

that groups similar observations together. Classification and clustering

are quite alike, but clustering is more concerned with exploration than

an end result.

Note: This post is far from an exhaustive look at all clustering has to

offer. Check out this guide

for more. I am reading Data Mining by Aggarwal presently, which is very informative.

data("iris") head(iris)

```
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
```

For simplicity, we’ll use the `iris`

dataset. We’re going to try to use

the numeric data to correctly group the observations by species. There

are 50 of each species in the dataset, so ideally we would end up with

three clusters of 50.

library(ggplot2) ggplot() + geom_point(aes(iris$Sepal.Length, iris$Sepal.Width, col = iris$Species))

As you can see, there is already some groupings present. Let’s use

hierarcical clustering first.

iris2 <- iris[,c(1:4)] #not going to use the `Species` column. medians <- apply(iris2, 2, median) mads <- apply(iris2,2,mad) iris3 <- scale(iris2, center = medians, scale = mads) dist <- dist(iris3) hclust <- hclust(dist, method = 'median') #there are several methods for hclust cut <- cutree(hclust, 3) table(cut)

```
## cut
## 1 2 3
## 49 34 67
```

iris <- cbind(iris, cut) iris$cut <- factor(iris$cut) levels(iris$cut) <- c('setosa', 'versicolor', 'virginica') err <- iris$Species == iris$cut table(err)

```
## err
## FALSE TRUE
## 38 112
```

ggplot() + geom_point(aes(iris2$Sepal.Length, iris2$Sepal.Width, col = iris$cut))

Nice groupings here, but it looks like the algorithm has some trouble

determining between versicolor and virginica. If we used this

information to classify the observations, we’d get an error rate of

about .25. Let’s try another clustering technique: DBSCAN.

library(dbscan)

db <- dbscan(iris3, eps = 1, minPts = 20) table(db$cluster)

```
##
## 0 1 2
## 5 48 97
```

iris2 <- cbind(iris2, db$cluster) iris2$cluster <- factor(iris2$`db$cluster`) ggplot() + geom_point(aes(iris2$Sepal.Length, iris2$Sepal.Width, col = iris2$cluster))

DBSCAN classifies points into three different categories: core, border,

and noise points on the basis of density. Thus, the versicolor/

virginica cluster is treated as one group. Since our data is not

structured in such a way that density is meaningful, DBSCAN is probably

not a wise choice here.

Let’s look at one last algo: the ROCK. No, not *that* ROCK.

library(cba)

distm <- as.matrix(dist) rock <- rockCluster(distm, 3, theta = .02)

```
## Clustering:
## computing distances ...
## computing links ...
## computing clusters ...
```

iris$rock <- rock$cl levels(iris$rock) <- c('setosa', 'versicolor', 'virginica') ggplot() + geom_point(aes(iris2$Sepal.Length, iris2$Sepal.Width, col = rock$cl))

err <- (iris$Species == iris$rock) table(err)

```
## err
## FALSE TRUE
## 24 126
```

While it may not look like it, the ROCK does the best job at determining

clusters in this data – the error rate dropped to 16%. Typically this

method is reserved for categorical data, but since we used `dist`

it

shouldn't cause any problems.

I have been working on a project using some of these (and similar) data

mining procedures to explore spatial data and search for distinct

groups. While clustering the `iris`

data may not be all that meaningful,

I think it is illustrative of the power of clustering. I have yet to try

higher-dimension clustering techniques, which might be even better at

determining `Species`

.

Have any comments? Questions? Suggestions for future posts? I am always

happy to hear from readers; please contact me!

Happy clustering,

Kiefer

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