# Clustergram¶

## Visualization and diagnostics for cluster analysis¶

Clustergram is a diagram proposed by Matthias Schonlau in his paper *The clustergram: A graph for visualizing hierarchical and nonhierarchical cluster analyses*.

*In hierarchical cluster analysis, dendrograms are used to visualize how clusters are formed. I propose an alternative graph called a “clustergram” to examine how cluster members are assigned to clusters as the number of clusters increases. This graph is useful in exploratory analysis for nonhierarchical clustering algorithms such as k-means and for hierarchical cluster algorithms when the number of observations is large enough to make dendrograms impractical.*

The clustergram was later implemented in R by Tal Galili, who also gives a thorough explanation of the concept.

This is a Python translation of Tal’s script written for `scikit-learn`

and RAPIDS `cuML`

implementations of K-Means, Mini Batch K-Means and Gaussian Mixture Model (scikit-learn only) clustering, plus hierarchical/agglomerative clustering using `SciPy`

. Alternatively, you can create clustergram using `from_*`

constructors based on alternative clustering algorithms.

## Getting started¶

You can install clustergram from `conda`

or `pip`

:

```
conda install clustergram -c conda-forge
```

```
pip install clustergram
```

In any case, you still need to install your selected backend
(`scikit-learn`

and `scipy`

or `cuML`

).

The example of clustergram on Palmer penguins dataset:

```
import seaborn
df = seaborn.load_dataset('penguins')
```

First we have to select numerical data and scale them.

```
from sklearn.preprocessing import scale
data = scale(df.drop(columns=['species', 'island', 'sex']).dropna())
```

And then we can simply pass the data to `clustergram`

.

```
from clustergram import Clustergram
cgram = Clustergram(range(1, 8))
cgram.fit(data)
cgram.plot()
```

## References¶

Schonlau M. The clustergram: a graph for visualizing hierarchical and non-hierarchical cluster analyses. The Stata Journal, 2002; 2 (4):391-402.

Schonlau M. Visualizing Hierarchical and Non-Hierarchical Cluster Analyses with Clustergrams. Computational Statistics: 2004; 19(1):95-111.