Applying Clustering

This section continues the example presented in the previous section on K-means. In addition to discussing code for implementing agglomerative clustering, it also includes applications of various accuracy measures useful for analyzing clutering performance. 

Clustering

The main idea behind clustering is pretty straightforward. Basically, we say to ourselves, "I have these points here, and I can see that they organize into groups. It would be nice to describe these things more concretely, and, when a new point comes in, assign it to the correct group". This general idea encourages exploration and opens up a variety of algorithms for clustering.


*The examples of the outcomes from different algorithms from scikit-learn*

The algorithms listed below do not cover all the clustering methods out there, but they are the most commonly used ones.

Source: Yury Kashnitsky, https://www.kaggle.com/code/kashnitsky/topic-7-unsupervised-learning-pca-and-clustering/notebook
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