Clustering Algorithms

Let's briefly introduce some main clustering algorithms. We'll focus on these in the course:

Python Libraries for Clustering

When you work with clustering in Python, you'll often use the following libraries:

• Scikit-learn: a comprehensive machine learning library. Scikit-learn provides implementations of many clustering algorithms, including K-means, Hierarchical Clustering, DBSCAN, and GMMs, as well as tools for data preprocessing, evaluation metrics, and more;

• SciPy: a library for scientific and technical computing. SciPy includes functions for hierarchical clustering, distance calculations, and other utilities that can be useful in clustering tasks.

There are also several auxiliary libraries that come in handy, such as NumPy (for numerical operations), Pandas (for data loading and preprocessing), Matplotlib, and Seaborn (for visualizing data and clustering results). While these aren't clustering libraries themselves, they support the overall workflow.