Unsupervised learning is a category of machine learning that entails training an algorithm on a dataset that has not been labeled or classified. The purpose is to recognize hidden patterns or fundamental formations in the information. Unsupervised learning methods include these:

Clustering: Clustering entails arranging a group of entities in such a manner that the objects in the same group are identical to each other but distinct from those in other groups. The most popular methods are K-Means, Hierarchical Clustering, and DBSCAN.

Principal Component Analysis: it is a method used to cut the dimensionality of data by transforming it into a new set of variables and principal components that are uncorrelated and maintain a significant amount of the original data. It’s used for dimensionality reduction.

Anomaly Detection: Here’s the process of detecting rare items, events or observations by searching for outliers that are significantly dissimilar from the majority of the data. The most SE techniques include Isolation Forest and One-Class SVM.

Association Rule Learning: Association rule learning is a technique for detecting casual relationships in large data sets. It includes analyzing transaction data to find connections based on the association of ite. Association rule learns interesting relationships between variables in large databases. Market basket analysis is a common application ; it’s used to identify relationships between items that customers buy together.

Autoencoders: Autoencoders are neural networks that learn to encode input data efficiently; they learn to compress the data to a lower-dimensional space and then try to reconstruct the data to its original form.

Self-Organizing Maps: Self-organizing maps are trained using unsupervised learning. to produce a two-dimensional, low-dimensional representation of your input space.

t-Distributed Stochastic Neighbor Embedding: t-SNE is a technique that is good for visualizing high-dimensional data . Independent Component Analysis.Material can be separated into various independent parts with ICA. It’s often used for Blind source separation.

Latent Dirichlet Allocation: LDA is a generative statistical model that explains why some parts of the data are similar and others are not. ; it was created for text analysis.

Generative Adversarial Networks: GAN comprise of two systems, the generator and the discriminator, which are prepared together.