Applying Clustering

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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