Summary  
This chapter introduces four clustering algorithms—K-means, agglomerative hierarchical clustering, DBSCAN, and Gaussian mixture models—and demonstrates how to implement them in code using Python libraries for numerical computation, data manipulation, and visualization.

General domain of usage  
Machine learning

## 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.

Which clustering algorithm is best suited for detecting clusters of arbitrary shape and identifying outliers?

Unlock the power of cluster analysis in Python. Group unlabeled data using K-Means, Hierarchical Clustering, and DBSCAN. Visualize clusters, interpret dendrograms, and evaluate results with Silhouette Score to reveal hidden patterns in your datasets.

Clustering Algorithms and Libraries

Clustering Algorithms

Python Libraries for Clustering