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 clustering (as of v0.1).

Getting started

pip install git+git://github.com/martinfleis/clustergram.git

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()

Styling

clustergram returns matplotlib axis and can be fully customised as any other matplotlib plot.

seaborn.set(style='whitegrid')

cgram.plot(
    ax=ax,
    size=0.5,
    linewidth=0.5,
    cluster_style={"color": "lightblue", "edgecolor": "black"},
    line_style={"color": "red", "linestyle": "-."},
    figsize=(12, 8)
)

Mean options

On the y axis, a clustergram can use mean values as in the original paper by Matthias Schonlau or PCA weighted mean values as in the implementation by Tal Galili.

cgram = Clustergram(range(1, 8), pca_weighted=True)
cgram.fit(data)
cgram.plot(figsize=(12, 8))

cgram = Clustergram(range(1, 8), pca_weighted=False)
cgram.fit(data)
cgram.plot(figsize=(12, 8))

Scikit-learn and RAPIDS cuML backends

Clustergram offers two backends for the computation - scikit-learn which uses CPU and RAPIDS.AI cuML, which uses GPU. Note that both are optional dependencies, but you will need at least one of them to generate clustergram.

Using scikit-learn (default):

cgram = Clustergram(range(1, 8), backend='sklearn')
cgram.fit(data)
cgram.plot()

Using cuML (default):

cgram = Clustergram(range(1, 8), backend='cuML')
cgram.fit(data)
cgram.plot()

data can be all data types supported by the selected backend (including cudf.DataFrame with cuML backend).

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/