Clustering
Access the notebooks with the option to run them online using Binder: 
The examples are organized below:
Data Normalization
It presents the use of the original (non-normalized) data and demonstrates how to normalize it between 0 and 1 using MinMaxScaler, preparing the dataset for training and enhancing clustering performance.
Training Model
Initializes and trains the model, identifies clusters, and evaluates clustering quality using metrics such as Silhouette Score and Adjusted Rand Index (ARI).
Visualizing Clusters
Visualizes the formed clusters, the antibody population, and the immune network.
Examples:
Immune Network Algorithms
On this page, you will find a collection of practical examples that demonstrate how to use the Immune Network Algorithm classes implemented in our package.