Several unsupervised learning algorithms are particularly useful in nanotechnology:
K-means Clustering: Used for partitioning data into k distinct clusters based on feature similarity. Principal Component Analysis (PCA): Helps in reducing the number of variables while retaining the most important information. Hierarchical Clustering: Builds a hierarchy of clusters for better understanding of data relationships. Autoencoders: Neural networks designed for dimensionality reduction and feature learning.
