How to Compare and Evaluate Unsupervised Clustering Methods?

Using Python, Scikit-Learn, and Google Colab

Evaluating the performance of unsupervised clustering methods is difficult because there are no ground truth labels to compare the clustering results with. Remember that clustering is an unsupervised learning task, meaning that it operates on unlabeled data and tries to find structure in the data without relying on pre-existing labels.

The solution?

Evaluation metrics for unsupervised clustering methods must rely on intrinsic properties of the data and clustering results, such as compactness and separation of the clusters, consistency with external knowledge, and stability of the results across different runs of the same algorithm.

In this article, I will provide a comprehensive overview of various evaluation methods available in the Scikit-Learn library for comparing different clustering techniques. The code for this article will be executed in Google Colab, and each step will be thoroughly documented. This is also a long read and will take you some time and effort, so be prepared!

Table of Contents:
  1. Create data
  2. Build Clustering Methods to Compare:
    - K-Means
    - Affinity Propagation
    - Agglomerative Clustering
    - Mean Shift Clustering…