The traditional clustering methods, such as hierarchical clustering and k-means clustering, are heuristic and are not based on formal models. Furthermore, k-means algorithm is commonly randomnly initialized, so different runs of k-means will often yield different results. Additionally, k-means requires the user to specify the the optimal number of clusters.

An alternative is **model-based clustering**, which consider the data as coming from a distribution that is mixture of two or more clusters (Fraley and Raftery 2002, Fraley et al. (2012)). Unlike k-means, the model-based clustering uses a soft assignment, where each data point has a probability of belonging to each cluster.

In this chapter, we illustrate model-based clustering using the R package *mclust*.

Contents:

#### Related Book

Practical Guide to Cluster Analysis in R## Concept of model-based clustering

In model-based clustering, the data is considered as coming from a mixture of density.

Each component (i.e. cluster) k is modeled by the normal or Gaussian distribution which is characterized by the parameters:

- \(\mu_k\): mean vector,
- \(\sum_k\): covariance matrix,
- An associated probability in the mixture. Each point has a probability of belonging to each cluster.

For example, consider the “old faithful geyser data” [in MASS R package], which can be illustrated as follow using the ggpubr R package:

```
# Load the data
library("MASS")
data("geyser")
# Scatter plot
library("ggpubr")
ggscatter(geyser, x = "duration", y = "waiting")+
geom_density2d() # Add 2D density
```

The plot above suggests at least 3 clusters in the mixture. The shape of each of the 3 clusters appears to be approximately elliptical suggesting three bivariate normal distributions. As the 3 ellipses seems to be similar in terms of volume, shape and orientation, we might anticipate that the three components of this mixture might have homogeneous covariance matrices.

## Estimating model parameters

The model parameters can be estimated using the *Expectation-Maximization* (EM) algorithm initialized by hierarchical model-based clustering. Each cluster k is centered at the means \(\mu_k\), with increased density for points near the mean.

Geometric features (shape, volume, orientation) of each cluster are determined by the covariance matrix \(\sum_k\).

Different possible parameterizations of \(\sum_k\) are available in the R package *mclust* (see *?mclustModelNames*).

The available model options, in *mclust* package, are represented by identifiers including: EII, VII, EEI, VEI, EVI, VVI, EEE, EEV, VEV and VVV.

The first identifier refers to volume, the second to shape and the third to orientation. E stands for “equal”, V for “variable” and I for “coordinate axes”.

For example:

- EVI denotes a model in which the volumes of all clusters are equal (E), the shapes of the clusters may vary (V), and the orientation is the identity (I) or “coordinate axes.
- EEE means that the clusters have the same volume, shape and orientation in p-dimensional space.
- VEI means that the clusters have variable volume, the same shape and orientation equal to coordinate axes.

## Choosing the best model

The *Mclust* package uses maximum likelihood to fit all these models, with different covariance matrix parameterizations, for a range of k components.

The best model is selected using the Bayesian Information Criterion or *BIC*. A large BIC score indicates strong evidence for the corresponding model.

## Computing model-based clustering in R

We start by installing the *mclust* package as follow: *install.packages(“mclust”)*

Note that, model-based clustering can be applied on univariate or multivariate data.

Here, we illustrate model-based clustering on the diabetes data set [mclust package] giving three measurements and the diagnosis for 145 subjects described as follow:

```
library("mclust")
data("diabetes")
head(diabetes, 3)
```

```
## class glucose insulin sspg
## 1 Normal 80 356 124
## 2 Normal 97 289 117
## 3 Normal 105 319 143
```

- class: the diagnosis: normal, chemically diabetic, and overtly diabetic. Excluded from the cluster analysis.
- glucose: plasma glucose response to oral glucose
- insulin: plasma insulin response to oral glucose
- sspg: steady-state plasma glucose (measures insulin resistance)

Model-based clustering can be computed using the function Mclust() as follow:

```
library(mclust)
df <- scale(diabetes[, -1]) # Standardize the data
mc <- Mclust(df) # Model-based-clustering
```

`summary(mc) # Print a summary`

```
## ----------------------------------------------------
## Gaussian finite mixture model fitted by EM algorithm
## ----------------------------------------------------
##
## Mclust VVV (ellipsoidal, varying volume, shape, and orientation) model with 3 components:
##
## log.likelihood n df BIC ICL
## -169 145 29 -483 -501
##
## Clustering table:
## 1 2 3
## 81 36 28
```

For this data, it can be seen that model-based clustering selected a model with three components (i.e. clusters). The optimal selected model name is VVV model. That is the three components are ellipsoidal with varying volume, shape, and orientation. The summary contains also the clustering table specifying the number of observations in each clusters.

You can access to the results as follow:

```
mc$modelName # Optimal selected model ==> "VVV"
mc$G # Optimal number of cluster => 3
head(mc$z, 30) # Probality to belong to a given cluster
head(mc$classification, 30) # Cluster assignement of each observation
```

## Visualizing model-based clustering

Model-based clustering results can be drawn using the base function plot.Mclust() [in mclust package]. Here we’ll use the function *fviz_mclust*() [in *factoextra* package] to create beautiful plots based on ggplot2.

In the situation, where the data contain more than two variables, *fviz_mclust*() uses a principal component analysis to reduce the dimensionnality of the data. The first two principal components are used to produce a scatter plot of the data. However, if you want to plot the data using only two variables of interest, let say here c(“insulin”, “sspg”), you can specify that in the *fviz_mclust*() function using the argument *choose.vars = c(“insulin”, “sspg”)*.

```
library(factoextra)
# BIC values used for choosing the number of clusters
fviz_mclust(mc, "BIC", palette = "jco")
# Classification: plot showing the clustering
fviz_mclust(mc, "classification", geom = "point",
pointsize = 1.5, palette = "jco")
# Classification uncertainty
fviz_mclust(mc, "uncertainty", palette = "jco")
```

Note that, in the uncertainty plot, larger symbols indicate the more uncertain observations.

## References

Fraley, Chris, and Adrian E Raftery. 2002. “Model-Based Clustering, Discriminant Analysis, and Density Estimation.” *Journal of the American Statistical Association* 97 (458): 611–31.

Fraley, Chris, Adrian E. Raftery, T. Brendan Murphy, and Luca Scrucca. 2012. “Mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation.” *Technical Report No. 597, Department of Statistics, University of Washington*. https://www.stat.washington.edu/research/reports/2012/tr597.pdf.

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Hi Chafia,

Thank you very much for the feedback.

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Hi Kas,

Thanks for sharing, like Chafia, I agree that this is very helpful.

I have .a quick question about the intuition around scale and using a distance matrix (or method). I find that in certain instance, data is scaled and in others, a distance method such as Bray Curtis or Jaccardi is used. Am referring to microbiome studies. What are your thoughts?

Thanks,

Hi San Emmanuel,

Thank you for your feedback!

The choice of the (dis)simality metric shoud be based on the research question and the type of dataset.

For example, Euclidian distance is best for variables with continuous data while Bray Curtis is best for categorical or binary data.

Particularly for continuous data it is expected that all variables are in the “same” scale and with the “same” distribution. So if your variables are not, you will need to standardize or normalize them.

If You want to reflect ecological differences, then Bray-Curtis will do a much better job, since it used to quantify the compositional dissimilarity between two different sites, based on counts at each site.

The Bray–Curtis dissimilarity is often erroneously called a distance. It is not a distance since it does not satisfy triangle inequality, and should always be called a dissimilarity to avoid confusion.

The use of Euclidean (metric distance) and Bray-Curtis (semi metric) depends on your data and the way you want to handle it. Metric distances comply with the triangle inequality criterion (the sum of two sides of a triangle equal must be greatet or equal than the other side) while semi metric don’t.

This is particularly relevant when zeros are not true absences (eg when you sample species from a site, you’ll never know for sure if the species is truly absent or you failed to sample it but is present, or in your case metals).

This is very important because if your zeros aren’t true absences and you use Euclidean distance, the dissimilarities among sites won’t be a good description of your data, that is, two sites with a bunch of shared zeros will be more similar to each other this two sites with a few shared observations. This is why, when dealing with composition data, it is more appropriate to use Bray-Curtis over Euclidean distance.

See also:

– Clustering Distance Measures, https://www.datanovia.com/en/lessons/clustering-distance-measures/