Visualization and diagnostics for cluster analysis in Python

Clustergram

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.

 
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