# 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
```

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.

https://www.r-statistics.com/2010/06/clustergram-visualization-and-diagnostics-for-cluster-analysis-r-code/